Showing posts with label adjunct breast imaging. Show all posts
Showing posts with label adjunct breast imaging. Show all posts
Thursday, February 4, 2016
Radiation-free Imaging
Radiation-free imaging is the wave of the future- safe, affordable and reliable Check it out now. Call for more information 855-482-6444
Tuesday, October 6, 2015
Beating Breast Cancer
William Hobbins, MD, FABS, DABCT, FIACT William Amalu, DC, DABCT, DIACT,
FIACT
This year, over 192,000 women will be diagnosed with breast cancer in the US and 1.2 million worldwide (Source: American Cancer Society and WHO). As shocking as these numbers are, even worse is the number of cancers that won’t be detected until it’s too late. The consensus among
experts is that early detection holds the key to survival. Although this is true, detection is not occurring early enough. Even though women are advised to begin having mammograms at 40, what they don’t know is that by the time most cancers are detected they have been growing for 10 years, and that 20% of all cancers can’t be seen by a mammogram. It is because of these factors, and others, that the number of women who die from this disease has gone relatively unchanged in the past 40 years.
A change from sole dependence upon procedures that only provide detection of existing cancers to technologies that reflect the early cancerous process itself would provide women with true screening.
If a significant change in breast cancer mortality is to be realized, we have to rethink what screening tests truly are. Are we currently providing “screening” or “detection”? A critical look at what we are
providing women must be made. If there were a method of very early detection, a procedure that would act as an early warning system, women would have the fighting chance they need to win this battle. What is needed is a biological risk marker. A biological risk marker would be able to turn these grave statistics around, as aggressive tissues would be detected before they were able to
invade the rest of the body. Women now have access to a unique technology that can give them this early warning; a procedure called Breast Thermography.
Breast thermography is an imaging technology that uses advanced computerized infrared camera systems to detect heat patterns in the breast. When a cancer is forming it develops its own blood supply in order to feed its accelerated growth (a process known as malignant angiogenesis). Even
more important, precancerous tissues can start this process well in advance of the cells becoming malignant. This increased Research has determined that the single greatest risk factor for the future development of breast cancer is lifetime exposure of the breasts to estrogen. In which case, controlling the influence of estrogen on the breasts would be the single greatest method of primary breast cancer prevention. Studies show that breast thermography has the ability to warn a woman that a cancer may be forming up to 10 years before any other test can detect it. blood supply causes an abnormal heat pattern in the breast. Thermography can detect this abnormal heat pattern by scanning the breasts with a specialized infrared camera and analyzing the information using sophisticated computer programs under the guidance of a doctor who is board certified in the procedure. These abnormal heat patterns are among the earliest known signs of a forming cancer.
Studies show that this technology has the ability to warn a woman that a cancer may be forming up to 10 years before any other test can detect it.
An unprecedented level of early detection can be realized when thermography is added to a
woman’s regular breast health care. It has been found that an abnormal thermographic image is the single most important sign of high risk for developing breast cancer, 10 times more significant than a first order family history of the disease. This gives breast thermography not only the ability to detect cancer at its earliest and most treatable stage, but to also act as a biological marker warning a woman about her own unique level of future risk for breast cancer.
Women who undergo the test find it to be fairly uneventful, since the procedure uses no radiation or contact with the breasts. Women with dense breasts, implants, and women who are pregnant or nursing can be imaged without any harm or reduction in the accuracy of the test. Normal images, show evenly cool inactive breasts (dark colors represent cold areas). Abnormal images, as seen on the right, show highly active blood vessels giving off heat in one breast. Since the procedure does not pose any harm to the patient, women who are at higher risk can be monitored closely without adverse effects on their health.
Research has determined that the single greatest risk factor for the future development of breast cancer is lifetime exposure of the breasts to estrogen. In which case, controlling the influence of estrogen on the breasts would be the single greatest method of primary breast cancer prevention.
Another benefit of this technology is its role in primary breast cancer prevention. Breast thermography has the added ability to observe the influence of hormones on the breasts. When hormone activity in the breast is dominated by estrogen, a specific type of infrared image is produced; thus, warning the patient of this condition. Once this is identified, a woman can take a significant pro-active role in prevention. With this information in hand, many doctors start their patients on a regimen of progesterone cream applied directly to the breasts. The progesterone enters
the breast tissue and counteracts the effects of estrogen. Using follow up infrared imaging, the treatment can be monitored and changed if necessary to meet the needs of each woman’s own unique physiology. Once the hormone balance has been restored to the breasts, a woman’s overall breast cancer risk is greatly reduced. The lifesaving implication of having this knowledge is incredible.
With the incidence of breast cancer steadily rising in women under 40, an effort to provide some form of accurate screening test is needed in this age group. Very early detection is especially important since breast cancers in younger women are commonly more aggressive resulting in lower survival rates. Current screening procedures have proven to be inaccurate in women in this age group due to breast tissue density and other factors. These issues, however, do not affect thermography. With this technology, women under 40 now have a safe and objective screening method that they can add to their regular breast health checkups.
Breast thermography is a high-tech non-invasive screening procedure designed to be used by women of all ages. The technology has been thoroughly researched for over 30 years and is FDA approved for use in breast cancer screening. Its unique ability to play a significant role in prevention is an impressive added benefit. Unfortunately, at this time there are too few qualified clinical thermography centers worldwide. However, with the increasing demand for breast thermography, recognized educational organizations, such as the International Academy of Clinical Thermology, are actively seeking personnel for training as certified technicians. It is their goal to provide women with greater access to this lifesaving technology.
Currently, no single screening procedure can detect 100% of all breast cancers. Thermography is designed to be used with mammography and not as a replacement. Studies show that when thermography is added to a woman’s regular breast health checkups (physical examination + mammography + thermography), 95% of all early stage cancers will be detected. This would give the vast majority of women who are diagnosed with this disease the reality of returning to a normal healthy life.
Should we continue to concentrate our efforts on procedures that can only detect an existing cancer, or should we be focusing on true screening methods that can warn of a pending problem far in advance? The number of women who die from this disease will continue relatively unchanged if nothing is done to provide them with a true early warning system. Breast thermography has the unique ability to warn most women far enough in advance to give them a fighting chance. Combined
with its ability to play a role in primary prevention, the lifesaving implications are incredible. The addition of this technology to every woman’s breast health care will make the greatest impact
on breast cancer mortality. With breast thermography, women of all ages are given hope and a true early detection edge in the battle against breast cancer.
About the authors-
William Hobbins, MD, a Fellow of the American Board of Surgeons and a board certified clinical thermologist, has been performing thermographic breast imaging for over 35 years. As an internationally recognized authority in this field, he has sat on multiple medical and thermographic boards, authored numerous articles, and has contributed a significant amount of research to the medical database using this technology. He currently practices in Madison Wisconsin and can be contacted at 608-273-4274.
William Amalu, DC, a Fellow of the International Academy of Clinical Thermology and a board certified clinical thermologist, has utilized thermography in practice for over 14 years. He is currently the President of the International Academy of Clinical Thermology and practices in Redwood City California. He can be contacted at 650-361-8908
www.breastthermography.com
http://www.stocktonfp.com/Articles/Beating%20Breast%20Cancer.pdf
William Hobbins, MD, FABS, DABCT, FIACT William Amalu, DC, DABCT, DIACT,
FIACT
This year, over 192,000 women will be diagnosed with breast cancer in the US and 1.2 million worldwide (Source: American Cancer Society and WHO). As shocking as these numbers are, even worse is the number of cancers that won’t be detected until it’s too late. The consensus among
experts is that early detection holds the key to survival. Although this is true, detection is not occurring early enough. Even though women are advised to begin having mammograms at 40, what they don’t know is that by the time most cancers are detected they have been growing for 10 years, and that 20% of all cancers can’t be seen by a mammogram. It is because of these factors, and others, that the number of women who die from this disease has gone relatively unchanged in the past 40 years.
A change from sole dependence upon procedures that only provide detection of existing cancers to technologies that reflect the early cancerous process itself would provide women with true screening.
If a significant change in breast cancer mortality is to be realized, we have to rethink what screening tests truly are. Are we currently providing “screening” or “detection”? A critical look at what we are
providing women must be made. If there were a method of very early detection, a procedure that would act as an early warning system, women would have the fighting chance they need to win this battle. What is needed is a biological risk marker. A biological risk marker would be able to turn these grave statistics around, as aggressive tissues would be detected before they were able to
invade the rest of the body. Women now have access to a unique technology that can give them this early warning; a procedure called Breast Thermography.
Breast thermography is an imaging technology that uses advanced computerized infrared camera systems to detect heat patterns in the breast. When a cancer is forming it develops its own blood supply in order to feed its accelerated growth (a process known as malignant angiogenesis). Even
more important, precancerous tissues can start this process well in advance of the cells becoming malignant. This increased Research has determined that the single greatest risk factor for the future development of breast cancer is lifetime exposure of the breasts to estrogen. In which case, controlling the influence of estrogen on the breasts would be the single greatest method of primary breast cancer prevention. Studies show that breast thermography has the ability to warn a woman that a cancer may be forming up to 10 years before any other test can detect it. blood supply causes an abnormal heat pattern in the breast. Thermography can detect this abnormal heat pattern by scanning the breasts with a specialized infrared camera and analyzing the information using sophisticated computer programs under the guidance of a doctor who is board certified in the procedure. These abnormal heat patterns are among the earliest known signs of a forming cancer.
Studies show that this technology has the ability to warn a woman that a cancer may be forming up to 10 years before any other test can detect it.
An unprecedented level of early detection can be realized when thermography is added to a
woman’s regular breast health care. It has been found that an abnormal thermographic image is the single most important sign of high risk for developing breast cancer, 10 times more significant than a first order family history of the disease. This gives breast thermography not only the ability to detect cancer at its earliest and most treatable stage, but to also act as a biological marker warning a woman about her own unique level of future risk for breast cancer.
Women who undergo the test find it to be fairly uneventful, since the procedure uses no radiation or contact with the breasts. Women with dense breasts, implants, and women who are pregnant or nursing can be imaged without any harm or reduction in the accuracy of the test. Normal images, show evenly cool inactive breasts (dark colors represent cold areas). Abnormal images, as seen on the right, show highly active blood vessels giving off heat in one breast. Since the procedure does not pose any harm to the patient, women who are at higher risk can be monitored closely without adverse effects on their health.
Research has determined that the single greatest risk factor for the future development of breast cancer is lifetime exposure of the breasts to estrogen. In which case, controlling the influence of estrogen on the breasts would be the single greatest method of primary breast cancer prevention.
Another benefit of this technology is its role in primary breast cancer prevention. Breast thermography has the added ability to observe the influence of hormones on the breasts. When hormone activity in the breast is dominated by estrogen, a specific type of infrared image is produced; thus, warning the patient of this condition. Once this is identified, a woman can take a significant pro-active role in prevention. With this information in hand, many doctors start their patients on a regimen of progesterone cream applied directly to the breasts. The progesterone enters
the breast tissue and counteracts the effects of estrogen. Using follow up infrared imaging, the treatment can be monitored and changed if necessary to meet the needs of each woman’s own unique physiology. Once the hormone balance has been restored to the breasts, a woman’s overall breast cancer risk is greatly reduced. The lifesaving implication of having this knowledge is incredible.
With the incidence of breast cancer steadily rising in women under 40, an effort to provide some form of accurate screening test is needed in this age group. Very early detection is especially important since breast cancers in younger women are commonly more aggressive resulting in lower survival rates. Current screening procedures have proven to be inaccurate in women in this age group due to breast tissue density and other factors. These issues, however, do not affect thermography. With this technology, women under 40 now have a safe and objective screening method that they can add to their regular breast health checkups.
Breast thermography is a high-tech non-invasive screening procedure designed to be used by women of all ages. The technology has been thoroughly researched for over 30 years and is FDA approved for use in breast cancer screening. Its unique ability to play a significant role in prevention is an impressive added benefit. Unfortunately, at this time there are too few qualified clinical thermography centers worldwide. However, with the increasing demand for breast thermography, recognized educational organizations, such as the International Academy of Clinical Thermology, are actively seeking personnel for training as certified technicians. It is their goal to provide women with greater access to this lifesaving technology.
Currently, no single screening procedure can detect 100% of all breast cancers. Thermography is designed to be used with mammography and not as a replacement. Studies show that when thermography is added to a woman’s regular breast health checkups (physical examination + mammography + thermography), 95% of all early stage cancers will be detected. This would give the vast majority of women who are diagnosed with this disease the reality of returning to a normal healthy life.
Should we continue to concentrate our efforts on procedures that can only detect an existing cancer, or should we be focusing on true screening methods that can warn of a pending problem far in advance? The number of women who die from this disease will continue relatively unchanged if nothing is done to provide them with a true early warning system. Breast thermography has the unique ability to warn most women far enough in advance to give them a fighting chance. Combined
with its ability to play a role in primary prevention, the lifesaving implications are incredible. The addition of this technology to every woman’s breast health care will make the greatest impact
on breast cancer mortality. With breast thermography, women of all ages are given hope and a true early detection edge in the battle against breast cancer.
About the authors-
William Hobbins, MD, a Fellow of the American Board of Surgeons and a board certified clinical thermologist, has been performing thermographic breast imaging for over 35 years. As an internationally recognized authority in this field, he has sat on multiple medical and thermographic boards, authored numerous articles, and has contributed a significant amount of research to the medical database using this technology. He currently practices in Madison Wisconsin and can be contacted at 608-273-4274.
William Amalu, DC, a Fellow of the International Academy of Clinical Thermology and a board certified clinical thermologist, has utilized thermography in practice for over 14 years. He is currently the President of the International Academy of Clinical Thermology and practices in Redwood City California. He can be contacted at 650-361-8908
www.breastthermography.com
http://www.stocktonfp.com/Articles/Beating%20Breast%20Cancer.pdf
Monday, September 21, 2015
Mammography (anatomical) and Thermography (physiological) A more effective screening combination for Early Detection?
Mammography (anatomical) and Thermography (physiological) A more effective screening combination for Early Detection?
• Information Source: www.breastthermography.com
EARLY DETECTION MEANS LIFE
Breast cancer is the most common cancer in women, and the risk increases with age (1).
Risk is also higher in women whose close relatives have had the disease. Women without
children, and those who have had their first child after age 30, also seem to be at higher
risk. However, every woman is at risk of developing breast cancer. Current research
indicates that 1 in every 8 women in the US will get breast cancer in their lifetime (1).
Studies show an increase in survival rate when breast thermography and
mammography are used together(3).
DII’s ability to detect thermal signs that may suggest a pre-cancerous state of the breast,
or signs of cancer at an extremely early stage, lies in its unique capability of monitoring the
temperature variations produced by the earliest changes in tissue physiology (function)
(3,6,7,8,9). However, DII does not have the ability to pinpoint the location of a tumor nor can it
detect 100% of all cancers. Consequently, Digital Infrared Imaging’s role is in addition (an
adjunct) to mammography and physical examination, not in lieu of. DII does not replace
mammography and mammography does not replace DII, the tests complement each other.
Since it has been determined that 1 in 8 women will get breast cancer, we must use every
means possible to detect cancers when there is the greatest chance for survival. Proper use
of breast self-exams, physician exams, DII, and mammography together provide the
earliest detection system available to date (3,7,8,9). If treated in the earliest stages, cure rates
greater than 95% are possible (3,6).
REFERENCES
1. American Cancer Society – Breast Cancer Guidelines and Statistics, 1999-2005
2. I. Nyirjesy, M.D. et al; Clinical Evaluation, Mammography and Thermography in the Diagnosis of Breast Carcinoma. Thermology, 1986; 1: 170-173.
3. M. Gautherie, Ph.D.; Thermobiological Assessment of Benign and Malignant Breast Diseases. Am. J. Obstet. Gynecol., 1983; V 147, No. 8: 861-869.
4. C. Gros, M.D., M. Gautherie, Ph.D.; Breast Thermography and Cancer Risk Prediction. Cancer, 1980; V 45, No. 1: 51-56.
5. P. Haehnel, M.D., M. Gautherie, Ph.D. et al; Long-Term Assessment of Breast Cancer Risk by Thermal Imaging. In: Biomedical Thermology, 1980; 279-301.
6. P. Gamigami, M.D.; Atlas of Mammography: New Early Signs in Breast Cancer. Blackwell Science, 1996.
7. J. Keyserlingk, M.D.; Time to Reassess the Value of Infrared Breast Imaging? Oncology News Int., 1997; V 6, No. 9.
8. P.Ahlgren, M.D., E. Yu, M.D., J. Keyserlingk, M.D.; Is it Time to Reassess the Value of Infrared Breast Imaging? Primary Care & Cancer (NCI), 1998; V 18, No. 2.
9. N. Belliveau, M.D., J. Keyserlingk, M.D. et al ; Infrared Imaging of the Breast: Initial Reappraisal Using High-Resolution Digital Technology in 100 Successive Cases of Stage I and II Breast Cancer. Breast Journal, 1998; V 4, No. 4
• Information Source: www.breastthermography.com
EARLY DETECTION MEANS LIFE
Breast cancer is the most common cancer in women, and the risk increases with age (1).
Risk is also higher in women whose close relatives have had the disease. Women without
children, and those who have had their first child after age 30, also seem to be at higher
risk. However, every woman is at risk of developing breast cancer. Current research
indicates that 1 in every 8 women in the US will get breast cancer in their lifetime (1).
Studies show an increase in survival rate when breast thermography and
mammography are used together(3).
DII’s ability to detect thermal signs that may suggest a pre-cancerous state of the breast,
or signs of cancer at an extremely early stage, lies in its unique capability of monitoring the
temperature variations produced by the earliest changes in tissue physiology (function)
(3,6,7,8,9). However, DII does not have the ability to pinpoint the location of a tumor nor can it
detect 100% of all cancers. Consequently, Digital Infrared Imaging’s role is in addition (an
adjunct) to mammography and physical examination, not in lieu of. DII does not replace
mammography and mammography does not replace DII, the tests complement each other.
Since it has been determined that 1 in 8 women will get breast cancer, we must use every
means possible to detect cancers when there is the greatest chance for survival. Proper use
of breast self-exams, physician exams, DII, and mammography together provide the
earliest detection system available to date (3,7,8,9). If treated in the earliest stages, cure rates
greater than 95% are possible (3,6).
REFERENCES
1. American Cancer Society – Breast Cancer Guidelines and Statistics, 1999-2005
2. I. Nyirjesy, M.D. et al; Clinical Evaluation, Mammography and Thermography in the Diagnosis of Breast Carcinoma. Thermology, 1986; 1: 170-173.
3. M. Gautherie, Ph.D.; Thermobiological Assessment of Benign and Malignant Breast Diseases. Am. J. Obstet. Gynecol., 1983; V 147, No. 8: 861-869.
4. C. Gros, M.D., M. Gautherie, Ph.D.; Breast Thermography and Cancer Risk Prediction. Cancer, 1980; V 45, No. 1: 51-56.
5. P. Haehnel, M.D., M. Gautherie, Ph.D. et al; Long-Term Assessment of Breast Cancer Risk by Thermal Imaging. In: Biomedical Thermology, 1980; 279-301.
6. P. Gamigami, M.D.; Atlas of Mammography: New Early Signs in Breast Cancer. Blackwell Science, 1996.
7. J. Keyserlingk, M.D.; Time to Reassess the Value of Infrared Breast Imaging? Oncology News Int., 1997; V 6, No. 9.
8. P.Ahlgren, M.D., E. Yu, M.D., J. Keyserlingk, M.D.; Is it Time to Reassess the Value of Infrared Breast Imaging? Primary Care & Cancer (NCI), 1998; V 18, No. 2.
9. N. Belliveau, M.D., J. Keyserlingk, M.D. et al ; Infrared Imaging of the Breast: Initial Reappraisal Using High-Resolution Digital Technology in 100 Successive Cases of Stage I and II Breast Cancer. Breast Journal, 1998; V 4, No. 4
Wednesday, August 26, 2015
New breast cancer screening guidelines released
New breast cancer screening guidelines released
Canadian Task Force on Preventive Health Care issues updated guidelines
New breast cancer screening guidelines for women at average risk of breast cancer, published in CMAJ(Canadian Medical Association Journal), recommend no routine mammography screening for women aged 40–49 and extend the screening interval from every 2 years, which is current
clinical practice, to every 2 to 3 years for women aged 50–74. The guidelines also recommend against routine clinical breast exam and breast self-examination in asymptomatic women.
The guidelines, aimed at physicians and policy-makers, provide recommendations for mammography, magnetic resonance imaging (MRI), breast self-exams and clinical breast exams by clinicians. They target average-risk women in three age groups (40–49, 50–69 and 70–74 years) who have not had breast cancer and do not have a family history of breast cancer in a mother, sister or daughter.
“As the Guideline on Breast Cancer Screening was last updated in 2001 and breast cancer screening has since become a subject for discussion amongst doctors and patients, the revitalized Canadian Task Force selected breast cancer screening as the topic for its first guideline,” said Dr. Marcello Tonelli, Chair of the Task Force on Preventive Health Care and Associate Professor at the University of Alberta, Department of Medicine, in Edmonton, Alberta. "We intend that this Guideline, which
reflects the latest scientific evidence in breast cancer screening, be used to guide physicians and their patients regarding the optimum use of mammograms and breast examination.”
According to the guideline, outcomes of breast cancer screening such as tumor detection and mortality must be put into context of the harms and costs of false–positive tests, over-diagnosis and over-treatment. False–positive results can have a significant impact on the emotional well-being of patients and families. They can cause lifestyle disruptions and result in costs to both patients and the health care system.
“Providing Canadians with guidelines that reflect the most current scientific evidence is our priority," said Dr. Tonelli. “We encourage every woman to discuss the risks and benefits of screening with their doctor before deciding on the best approach for them.”
Key recommendations:
No routine mammography for women aged 40-49 because the risk of cancer is low in this group while the risk of false–positive results and over-diagnosis and over-treatment is higher
Routine screening with mammography every two to three years for women aged 50-69
Routine screening with mammography every two to three years for women aged 70-74
No screening of average-risk women using MRI
No routine clinical breast exams or breast self-exam to screen for breast cancer.
“There was no evidence that screening with mammography reduces the risk of all-cause mortality,” state the authors. “Although screening might permit surgery for breast cancer at an earlier stage than diagnosis of clinically evident cancer (thus permitting the use of less invasive procedures for some women), available trial data suggest that the overall risk of mastectomy is significantly increased among recipients of screening compared with women who have not undergone screening.”
In addition to the full guidelines, one-page information pieces are available for both physicians and patients on the task force website: www.canadiantaskforce.ca
The Canadian Task Force on Preventive Health Care is an independent body of 14 primary care and prevention experts. The task force has been established by the Public Health Agency of Canada to develop clinical practice guidelines that support primary care providers in delivering preventive health care.
In a related commentary, Dr. Peter Gøtzsche, Nordic Cochrane Centre, Copenhagen, Denmark, writes, “these guidelines are more balanced and more in accordance with the evidence than any previous recommendations.”
He states that evidence does not support mammography screening and argues that screening is ineffective and even harmful because diagnosis of cancers that would otherwise be undetected lead to life-shortening treatments and mastectomies.
“The main effect of screening is to produce patients with breast cancer from among healthy women who would have remained free of breast disease for the rest of their lives had they not undergone screening,” writes Dr. Gøtzsche.
“The best method we have to reduce the risk of breast cancer is to stop the screening program,” he concludes. “This could reduce the risk by one-third in the screened age group, as the level of overdiagnosis in countries with organized screening programs is about 50%.”
MEDIA NOTE: Please use the following public links after the embargo lift:
Research http://www.cmaj.ca/lookup/doi/10.1503/cmaj.110334
Commentary http://www.cmaj.ca/lookup/doi/10.1503/cmaj.111721
Media contact for guidelines:
David Rodier
Hill & Knowlton
(613) 786 9945
david.rodier@hillandknowlton.ca
Canadian Task Force on Preventive Health Care issues updated guidelines
New breast cancer screening guidelines for women at average risk of breast cancer, published in CMAJ(Canadian Medical Association Journal), recommend no routine mammography screening for women aged 40–49 and extend the screening interval from every 2 years, which is current
clinical practice, to every 2 to 3 years for women aged 50–74. The guidelines also recommend against routine clinical breast exam and breast self-examination in asymptomatic women.
The guidelines, aimed at physicians and policy-makers, provide recommendations for mammography, magnetic resonance imaging (MRI), breast self-exams and clinical breast exams by clinicians. They target average-risk women in three age groups (40–49, 50–69 and 70–74 years) who have not had breast cancer and do not have a family history of breast cancer in a mother, sister or daughter.
“As the Guideline on Breast Cancer Screening was last updated in 2001 and breast cancer screening has since become a subject for discussion amongst doctors and patients, the revitalized Canadian Task Force selected breast cancer screening as the topic for its first guideline,” said Dr. Marcello Tonelli, Chair of the Task Force on Preventive Health Care and Associate Professor at the University of Alberta, Department of Medicine, in Edmonton, Alberta. "We intend that this Guideline, which
reflects the latest scientific evidence in breast cancer screening, be used to guide physicians and their patients regarding the optimum use of mammograms and breast examination.”
According to the guideline, outcomes of breast cancer screening such as tumor detection and mortality must be put into context of the harms and costs of false–positive tests, over-diagnosis and over-treatment. False–positive results can have a significant impact on the emotional well-being of patients and families. They can cause lifestyle disruptions and result in costs to both patients and the health care system.
“Providing Canadians with guidelines that reflect the most current scientific evidence is our priority," said Dr. Tonelli. “We encourage every woman to discuss the risks and benefits of screening with their doctor before deciding on the best approach for them.”
Key recommendations:
No routine mammography for women aged 40-49 because the risk of cancer is low in this group while the risk of false–positive results and over-diagnosis and over-treatment is higher
Routine screening with mammography every two to three years for women aged 50-69
Routine screening with mammography every two to three years for women aged 70-74
No screening of average-risk women using MRI
No routine clinical breast exams or breast self-exam to screen for breast cancer.
“There was no evidence that screening with mammography reduces the risk of all-cause mortality,” state the authors. “Although screening might permit surgery for breast cancer at an earlier stage than diagnosis of clinically evident cancer (thus permitting the use of less invasive procedures for some women), available trial data suggest that the overall risk of mastectomy is significantly increased among recipients of screening compared with women who have not undergone screening.”
In addition to the full guidelines, one-page information pieces are available for both physicians and patients on the task force website: www.canadiantaskforce.ca
The Canadian Task Force on Preventive Health Care is an independent body of 14 primary care and prevention experts. The task force has been established by the Public Health Agency of Canada to develop clinical practice guidelines that support primary care providers in delivering preventive health care.
In a related commentary, Dr. Peter Gøtzsche, Nordic Cochrane Centre, Copenhagen, Denmark, writes, “these guidelines are more balanced and more in accordance with the evidence than any previous recommendations.”
He states that evidence does not support mammography screening and argues that screening is ineffective and even harmful because diagnosis of cancers that would otherwise be undetected lead to life-shortening treatments and mastectomies.
“The main effect of screening is to produce patients with breast cancer from among healthy women who would have remained free of breast disease for the rest of their lives had they not undergone screening,” writes Dr. Gøtzsche.
“The best method we have to reduce the risk of breast cancer is to stop the screening program,” he concludes. “This could reduce the risk by one-third in the screened age group, as the level of overdiagnosis in countries with organized screening programs is about 50%.”
MEDIA NOTE: Please use the following public links after the embargo lift:
Research http://www.cmaj.ca/lookup/doi/10.1503/cmaj.110334
Commentary http://www.cmaj.ca/lookup/doi/10.1503/cmaj.111721
Media contact for guidelines:
David Rodier
Hill & Knowlton
(613) 786 9945
david.rodier@hillandknowlton.ca
Wednesday, August 12, 2015
Medical Infrared Imaging of the Breast: An Analysis of 100 Successive Cases of Breast Cancer
Medical Infrared Imaging of the Breast:
An Analysis of 100 Successive Cases of Breast
Cancer
William C. Amalu, DC, DABCT, FIACT
PCRC Infrared
Imaging Lab – Redwood City,
California
March 18,
2015
The following data presents the findings
in 100 successive cases of breast cancer using medical infrared imaging (MIR).
Thermovascular markers were detected using a specialized high-resolution
computerized medical infrared imaging system capable of detecting minute
variations in the regional vascular perfusion of the microdermal circulation. The
imaging system used is composed of a highly sensitive infrared camera coupled to
a central processing unit capable of multitasking capabilities including post-image
processing and accurate temperature measurements (Spectron IR 640 Medical
Infrared Imaging System). All pre-imaging patient preparation protocols and
laboratory requirements were strictly adhered to as per established MIR
standards and guidelines.
Following MIR interpretation guidelines,
each patient was referred back to their primary care provider with
recommendations for follow-up imaging or testing. The final diagnosis in each
case was made by biopsy.
In the data presented is a special
category of patients. In this group MIR was the first alarm that anything was
wrong. If it were not for MIR all of these patients would not have known they
had breast cancer.
When analyzing each
breast, 20 basic thermal attributes are used in the grading process. A computerized
analysis of both thermovascular patterns and objective temperature values are compared
to a normative database. This determines where each breast is graded into one
of five thermobiological classifications:
TH1 – uniform non-vascular
TH2 – uniform vascular
TH3 – questionable
TH4 – abnormal
TH5 – very abnormal
The following is a summary of the MIR findings in 100 successive
cases of breast cancer –
Thermobiological Grade
3 (TH3 – questionable): 22 cases
·
Of the 22 cases, 10 were “first alarm”
thermograms.
·
Of the 22 cases, only 9 were true TH3s. The
remaining 13 cases were TH3+ (TH3+ thermograms are almost TH4s)
·
Of the 22 cases, 2 cases had bilateral breast
cancer graded TH3 in both breasts.
·
One case was only 28 years old. This was the
youngest patient in all of the 100 cases.
·
Of the 22 cases, 10 cancers were in the right
breast and 12 in the left breast.
Thermobiological Grade
4 (TH4 – abnormal): 43 cases
·
Of the 43 cases, 22 were “first alarm”
thermograms.
·
Of the 43 cases, 1 case had bilateral breast
cancer graded TH3 in one breast and TH4 in the other.
·
Of the 43 cases, 1 patient was pregnant.
·
Of the 43 cases, 1 patient had a 3 year
lead-time thermogram warning.
·
Of the 43 cases, 14 cancers were in the right
breast and 29 in the left breast.
Thermobiological Grade
5 (TH5 – very abnormal): 35 cases
·
Of the 35 cases, 18 were “first alarm”
thermograms.
·
One case was only 36 years old.
·
Of the 35 cases: 3 were TH6s, 2 were TH7s, 2
were TH8s, and 1 case was a TH9
·
Of the 35 cases, 1 patient had a 4 year
lead-time thermogram warning.
·
Of the 43 cases, 13 cancers were in the right
breast and 22 in the left breast.
Summary –
Of the 100 cases 22% were TH3 (questionable),
43% were TH4 (abnormal), and 35% were TH5 (very abnormal). As such, 78% of the
cases were TH4 or TH5 abnormals. This closely approaches the published
literature stating that approximately 85% of all breast cancers are found in
the TH4-5 range. If we were to adjust the data to include only the true TH3
thermograms, 92% of the breast cancer cases in this group would have been found
in the abnormal range.
Of note, 37% of the breast cancers were
found in the right breast while 63% of the cancers were discovered in the left
breast. This agrees with the literature that the vast majority of breast
cancers are found in the left breast.
The 22% of all cases found in the TH3
range points out the importance of making sure that if recent structural
imaging has not been done that all TH3 graded thermograms are followed up with
structural imaging.
Of greatest importance is the 50% of the
women who had “first alarm” thermograms. All of these women would not have
known they had breast cancer if it were not for MIR. Many of whom would have
gone from a year to many years before having any other imaging done. How many
of these women would have died if not for this technology? How many breasts
were conserved due to MIR? What about the women in this group who were under 40
years of age? Cancers in this age group are usually more aggressive and have
poorer survival rates.
As an adjunctive imaging technology, MIR
offers every woman the possibility of earlier detection. The unique capability
of MIR may also play a significant role in prevention. Studies continue to
demonstrate that the addition of MIR to every woman’s regular breast health
care increases survival rates along with preserving the breast.
Wednesday, June 24, 2015
Wednesday, June 17, 2015
Am J Surg. 2008 Oct;196(4):523-6. Links
Effectiveness of a noninvasive digital infrared thermal imaging system in the detection of breast cancer.
Arora N, Martins D, Ruggerio D, Tousimis E, Swistel AJ, Osborne MP, Simmons
RM. Department of Surgery, New York Presbyterian Hospital-Cornell, New York, NY, USA.
BACKGROUND: Medical infrared thermal imaging (MITI) has resurfaced in this era of
modernized computer technology. Its role in the detection of breast cancer is evaluated.
METHODS: In this prospective clinical trial, 92 patients for whom a breast biopsy was
recommended based on prior mammogram or ultrasound underwent MITI. Three scores
were generated: an overall risk score in the screening mode, a clinical score based on
patient information, and a third assessment by artificial neural network.
RESULTS: Sixty of 94 biopsies were malignant and 34 were benign. MITI identified 58 of 60
malignancies, with 97% sensitivity, 44% specificity, and 82% negative predictive value
depending on the mode used. Compared to an overall risk score of 0, a score of 3 or greater was significantly more likely to be associated with malignancy (30% vs 90%, P <.03).
CONCLUSION: MITI is a valuable adjunct to mammography and ultrasound,
especially in women with dense breast parenchyma.
Effectiveness of a noninvasive digital infrared thermal imaging system in the detection of breast cancer.
Arora N, Martins D, Ruggerio D, Tousimis E, Swistel AJ, Osborne MP, Simmons
RM. Department of Surgery, New York Presbyterian Hospital-Cornell, New York, NY, USA.
BACKGROUND: Medical infrared thermal imaging (MITI) has resurfaced in this era of
modernized computer technology. Its role in the detection of breast cancer is evaluated.
METHODS: In this prospective clinical trial, 92 patients for whom a breast biopsy was
recommended based on prior mammogram or ultrasound underwent MITI. Three scores
were generated: an overall risk score in the screening mode, a clinical score based on
patient information, and a third assessment by artificial neural network.
RESULTS: Sixty of 94 biopsies were malignant and 34 were benign. MITI identified 58 of 60
malignancies, with 97% sensitivity, 44% specificity, and 82% negative predictive value
depending on the mode used. Compared to an overall risk score of 0, a score of 3 or greater was significantly more likely to be associated with malignancy (30% vs 90%, P <.03).
CONCLUSION: MITI is a valuable adjunct to mammography and ultrasound,
especially in women with dense breast parenchyma.
Wednesday, June 10, 2015
Evaluation of digital infra-red thermal imaging as an adjunctive screening method for breast carcinoma: A pilot study
Int J Surg. 2014 Dec;12(12):1439-43. doi: 10.1016/j.ijsu.2014.10.010. Epub 2014 Nov 7.
Evaluation of digital infra-red thermal imaging as an adjunctive screening method for breast carcinoma: A pilot study.
Rassiwala M1, Mathur P2, Mathur R3, Farid K3, Shukla S3, Gupta PK4, Jain B4. Author information
Abstract BACKGROUND: Early screening plays a pivotal role in management of breast cancer. Given the socio-economic situation in India, there is a strong felt need for a screening tool which reaches the masses rather than waiting for the masses to reach tertiary centers to be screened. Medical infra-red thermal imaging (MITI) or breast thermography as a screening test offers this possibility and needs to be carefully assessed in Indian scenario.
METHODS: The study involved 1008 female patients of age 20-60 years that had not been diagnosed of cancer of breast earlier. All the subjects in this population were screened for both the breasts using MITI. Based on the measured temperature gradients (ΔT) in thermograms, the subjects were classified in one of the three groups, normal (ΔT ≤ 2.5), abnormal (ΔT > 2.5, <3) and potentially having breast cancer (ΔT ≥ 3). All those having (ΔT > 2.5) underwent triple assessment that consisted of clinical examination, radiological and histopathological examination. Those with normal thermograms were subjected to only clinical examination.
RESULTS: Forty nine female breasts had thermograms with temperature gradients exceeding 2.5 and were subjected to triple assessment. Forty one of these which had ΔT ≥ 3 were proven to be having cancer of breast and were offered suitable treatment. Eight thermograms had temperature gradients exceeding 2.5 but less than 3. Most of these were lactating mothers or had fibrocystic breast diseases. As a screening modality, MITI showed sensitivity of 97.6%, specificity of 99.17%, positive predictive value 83.67% and negative predictive value 99.89%.
CONCLUSION: Based on the results of this study involving 1008 subjects for screening of breast cancer, thermography turns out to be a very useful tool for screening. Because it is non-contact, pain-free, radiation free and comparatively portable it can be used in as a proactive technique for detection of breast carcinoma.
Evaluation of digital infra-red thermal imaging as an adjunctive screening method for breast carcinoma: A pilot study.
Rassiwala M1, Mathur P2, Mathur R3, Farid K3, Shukla S3, Gupta PK4, Jain B4. Author information
Abstract BACKGROUND: Early screening plays a pivotal role in management of breast cancer. Given the socio-economic situation in India, there is a strong felt need for a screening tool which reaches the masses rather than waiting for the masses to reach tertiary centers to be screened. Medical infra-red thermal imaging (MITI) or breast thermography as a screening test offers this possibility and needs to be carefully assessed in Indian scenario.
METHODS: The study involved 1008 female patients of age 20-60 years that had not been diagnosed of cancer of breast earlier. All the subjects in this population were screened for both the breasts using MITI. Based on the measured temperature gradients (ΔT) in thermograms, the subjects were classified in one of the three groups, normal (ΔT ≤ 2.5), abnormal (ΔT > 2.5, <3) and potentially having breast cancer (ΔT ≥ 3). All those having (ΔT > 2.5) underwent triple assessment that consisted of clinical examination, radiological and histopathological examination. Those with normal thermograms were subjected to only clinical examination.
RESULTS: Forty nine female breasts had thermograms with temperature gradients exceeding 2.5 and were subjected to triple assessment. Forty one of these which had ΔT ≥ 3 were proven to be having cancer of breast and were offered suitable treatment. Eight thermograms had temperature gradients exceeding 2.5 but less than 3. Most of these were lactating mothers or had fibrocystic breast diseases. As a screening modality, MITI showed sensitivity of 97.6%, specificity of 99.17%, positive predictive value 83.67% and negative predictive value 99.89%.
CONCLUSION: Based on the results of this study involving 1008 subjects for screening of breast cancer, thermography turns out to be a very useful tool for screening. Because it is non-contact, pain-free, radiation free and comparatively portable it can be used in as a proactive technique for detection of breast carcinoma.
Wednesday, February 4, 2015
Motion tracking in infrared imaging for quantitative medical diagnostic applications.
Motion tracking in infrared imaging for quantitative medical diagnostic applications.
Author information
Abstract
In medical applications, infrared (IR) thermography is used to detect and examine the thermal signature of skin abnormalities by quantitatively analyzing skin temperature in steady state conditions or its evolution over time, captured in an image sequence. However, during the image acquisition period, the involuntary movements of the patient are unavoidable, and such movements will undermine the accuracy of temperature measurement for any particular location on the skin. In this study, a tracking approach using a template-based algorithm is proposed, to follow the involuntary motion of the subject in the IR image sequence. The motion tacking will allow to associate a temperature evolution to each spatial location on the body while the body moves relative to the image frame. The affine transformation model is adopted to estimate the motion parameters of the template image. The Lucas-Kanade algorithm is applied to search for the optimized parameters of the affine transformation. A weighting mask is incorporated into the algorithm to ensure its tracking robustness. To evaluate the feasibility of the tracking approach, two sets of IR image sequences with random in-plane motion were tested in our experiments. A steady-state (no heating or cooling) IR image sequence in which the skin temperature is in equilibrium with the environment was considered first. The thermal recovery IR image sequence, acquired when the skin is recovering from 60-s cooling, was the second case analyzed. By proper selection of the template image along with template update, satisfactory tracking results were obtained for both IR image sequences. The achieved tracking accuracies are promising in terms of satisfying the demands imposed by clinical applications of IR thermography.1. Introduction
1.1. Background
IR thermography is a non-ionizing and non-invasive imaging modality
that has been regaining interest in clinical medicine in recent years. Such
resurgence of interest can be attributed to the dramatic advances of IR camera
and computer technology, novel image processing algorithms, and the progress in
IR sensors since the 1990s. The availability of high sensitivity, high
resolution IR detectors at a reasonable cost, along with miniaturization of the
cameras, enabled the development of low-cost, portable systems suitable for
quantitative diagnostic applications in medicine. IR diagnostic techniques rely
on the hypothesis that a distinct thermal signature associated with a medical
condition can be detected and quantified using modern camera technology.
Quantitative imaging requires high accuracy – high sensitivity and high spatial
resolution – measurements, which can be static or dynamic measurements,
depending on the application. The common goal in thermographic diagnostic
applications is to measure skin temperature as a function of location and time
with high accuracy. These measurements are complex, since the shape of the
surface, the surface properties and the motion of the subject will influence
the temperature data derived from signals captured by the sensor. This paper
addresses the influence of the motion of the subject on IR temperature
measurements and proposes methods to compensate for these in order to achieve
high-accuracy temperature measurements.
Infrared imaging can be implemented either as a static or as a dynamic
technique in medical diagnostic applications. In static imaging applications a
steady state situation is analyzed: the subject is typically exposed to normal
ambient conditions and the spatial distribution of thermal contrasts on the
body is measured and analyzed. Dynamic IR imaging detects both spatial and
temporal variations of the emitted IR radiation, and this signal is related to
skin temperature during postprocessing. Prior to image acquisition, a thermal
excitation, such as cooling or heating, is applied to skin surface in dynamic
IR imaging. During and following the thermal excitation a sequence of
consecutive image frames is acquired, and the motion of the subject during the
image acquisition poses a significant challenge for accurate surface
temperature measurements. The phase after the removal of the thermal excitation
is the thermal recovery process, which is frequently analyzed in medical
imaging applications. By analyzing the thermal recovery of the skin
temperature, with temperature variations in the range of hundreds of
millikelvins, abnormalities such as the malignancy of skin lesions can be
examined, quantified and potentially diagnosed (with appropriate calibration
and clinical validation) [1–7].During the acquisition period, the patients’ involuntary movements (breathing or small involuntary movements of the body and limbs) will hinder the accurate temperature recording at any particular point on the skin, therefore undermining the validity and accuracy of the local temperature analysis and the associated diagnosis. As shown in Fig. 1, in clinical applications of medical IR imaging [2,4], the patient is typically motionless, positioned on a fixed exam chair or bed, and the IR camera is oriented towards the lesion on the skin with the camera axis perpendicular to the skin surface. Since the out-of-plane movements of the patient are limited and restrained by the exam chair, they result in a predominantly linear in-plane motion in the image sequence, as illustrated in Fig. 2. This linear in-plane motion is considered to be the major source of motion encountered in the clinical IR image analysis.
Schematic of subject's motion due to respiration and small
involuntary movements of the body and limbs in the clinical IR imaging
environment with the patient positioned in the exam chair.
Sample infrared images illustrating the subject's in-plane
motion in the acquired IR image sequence. The white arrows show the motion
direction relative the previous frame (the adjacent frame on the left hand
side), the rectangle is the template of known ...
Although such in-plane motion is relatively small and limited in
range, it still hinders the successive measurement of temperature at a
particular location on the skin surface in the IR image sequence. For example,
early stage melanoma lesions are small (of the order of a few millimeters), and
therefore movements of the order of a millimeter will hinder the localization
of any subtle thermal features [5].
The measured temperature differences are often very small as well (fraction of
a degree) and the feature to be analyzed (the lesion) cannot be detected
directly in the IR image, prior to image processing. In static IR imaging, in
which the temperature of the skin is approximately constant over time, accurate
tracking can locate the lesion automatically in the IR image without the aid of
a visible landmark. In dynamic IR imaging the temperature of the skin and the
signal detected by the camera sensor change over the time [2–4].
In such applications motion tracking is crucial to record the temperature
evolution accurately over the image sequence.
1.2. Literature review
Tracking a moving object in an image sequence is an active topic in
civil and military applications. Initial applications were developed for white
light imaging, however, motion and target tracking in IR image sequences has
become the subject of increasing interest in recent years as well. Due to the
lower cost and fast improvement of infrared (IR) technology, object tracking
has also been widely used in IR imaging, such as pedestrian detection for
surveillance purposes [8–10].
In IR surveillance applications the target typically stands out against the
background, it often covers a relatively small fraction of the total image
frame area, the range of motion is large and accurate temperature information
is generally not of interest.
Only a few researchers have tackled the motion tracking challenge in IR
imaging in medical applications [5,6].
The key difference between surveillance and medical applications is the smaller
thermal contrast between the diseased and healthy tissue, which is often not
detectable without sophisticated image processing. Also, the target often
covers a significant portion of the image frame. The range of motion of the
subject is smaller and accurate quantitative detection of temporal as well as
spatial variations is essential. Most of the past clinical applications of IR
imaging focused on breast cancer detection. To enable the wide use of dynamic
IR imaging in breast cancer screening [11–13],
the motion artifact reduction approach using image sequence realignment was proposed
[14–16]
with marker-based image registration. Image registration involves transforming
different data sets into one coordinate system. In medical imaging and computer
vision applications registration is essential in order to allow the comparison
or integration of data obtained from different measurements and sources, in our
application from white light and infrared images. In breast cancer imaging [14–16],
the registration of each IR image frame is achieved by aligning 5–18 markers
(which are directly visible in the successive images) to serve as control
points in the IR image sequence. The approach can achieve accurate tracking
performance in terms of signal to noise ratio (SNR).In the Heat Transfer Lab of Johns Hopkins University, we have developed a dynamic IR imaging system for the detection of melanoma [2–7]. The system relies on thermo-stimulation with external cooling and compares the transient thermal response of cancerous lesions and healthy skin. As shown by Cetingul et al. [5] and Herman [6], based on the quadratic motion model for landmark-based registration, a sequence of IR images acquired during the thermal recovery phase can be aligned to compensate for involuntary motion of the patient. The motion compensation enables an accurate measurement of temperature differences between lesions and healthy skin, and provides quantitative information to identify the malignancy of lesions. Without motion tracking, the measurement errors are too large to detect temperature differences that are indicative of malignancy [4–6]. Based on the experiences gained in our previous clinical studies, in this study, a template-based tracking scheme is applied for the dynamic IR imaging.
In the field of computer vision, automatic methods for tracking the moving object in an image sequence fall into three main categories [17,18]: feature-based tracking [19,20], contour-based tracking [21,22], and region-based tracking [23,24]. In the absence of occlusions, region-based tracking methods perform well in terms of robustness and accuracy. The template-based tracking [25] falls into the category of region-based tracking methods. When compared with other region-based methods with non-parametric description of the region's content [26], the template-based tracking directly uses a region content of the image to track the moving object. This is accomplished by extracting a template region in the first frame and finding the most matching region in the following frames. In this way the moving object can be tracked in a video sequence.
The template-based tracking method is originally based on the
assumption that the object's appearance remains constant throughout the video
sequence. However, in practical IR imaging applications this assumption often
holds only for a certain period of time. The appearance of the object would
change significantly with time and changes in the environment, for example as
the temperature of the object changes during thermal recovery in dynamic IR
imaging. Considering the tracking error due to the violation of initial assumption,
improvements in the tracking algorithm, including template update [27]
and the inclusion of robust weighting mask for template matching [18],
are proposed in this paper. Based on our preliminary results [28],
in the study we introduce the tracking method and validate it on two characteristic
classes of problems relevant for medical applications, the static and dynamic
imaging of skin temperature.
2. Algorithms and methods
2.1. Template-based tracking algorithm
In the clinical application of IR imaging a marker (a rectangular
shape applied to skin in our experiment, Fig. 3b) is commonly used for
the registration of the skin lesion [2,4].
This marker can simultaneously serve as the landmark of consistent appearance
in the template region. The template image is a sub-region of an image which
contains the object (skin lesion, for example) to be tracked in the image
sequence. Template-based tracking is achieved by estimating coordinate alignment
between the template image and the consecutive frames in a given video
sequence. Since the involuntary movements of patient are mainly in-plane
motions with a limited range, the template-based method is well suited for
motion tracking in medical IR imaging. In this paper, the method is described
using the notation of Matthews et al. [27].
(a) The Merlin midwave infrared camera and the IR image
acquisition system; (b) Two paper adhesive markers: the square one serves as
the tracking template and the round one represents the simulated lesion for
error analysis; (c) IR image of the adhesive ...
The alignment of images in a sequence can be parameterized as a warp
function, which transforms the pixel coordinate x in the template
image to a new coordinate W(x;p) in the subsequent
image frame. In this expression p denotes the transformation
parameters p = (p1,. . .,pn)T
of the warp function. In our application, a sub-region containing the object in
the initial IR image frame I0(x) is extracted to
be the template image T(x). In a subsequent image frame I(x),
the template image content at pixel x:T(x), will be
warped by W(x;p) and presented as I(W(x;p)).
It would have similar content as its correspondence in the template image if
the tracking is valid. Therefore, the optimized parameters p will be
searched to find the best match as
I(W(x;p))≈T(x).
(1)
In template-based tracking, the 2D affine transformation is utilized as the
warp function W(x;p). Linear transformations,
including translation, rotation, shear mapping, and scaling, are all taken into
account, and any two parallel lines will remain parallel after the
transformation. The affine warp consists of 6 independent parameters (Section
2.2):p = (p1, p2, p3,
p4, p5, p6)T
to model the transformation as
W(x;p)=(1+p1p2p31+p4p5p6)⎛⎝xy1⎞⎠,
where (p5, p6) are two parameters
describing translation, (p1, p4) are
two parameters for the scaling of x and y, and (p2,
p3) describe the angular change of each axis after warping.2.2. Lucas–Kanade approach
The first use of image alignment with a template reported in the technical literature is the Lucas–Kanade optical flow algorithm [25]. Since this work, the approach has become one of the most widely used methods in object tracking. In the remainder of this section, the notation of Baker and Matthews [29] is used to describe the algorithm. In order to search for the optimized parameters p for the warp function, the objective of Lucas–Kanade algorithm is to minimize the sum of square errors between the image content of I(W(x;p)) and the template T(x) as
∑x[I(W(x;p))−T(x)]2.
(3)
The minimization of Eq.
(3) is a non-linear optimization problem. Based on a current
estimate p for the warping parameters, the Lucas–Kanade algorithm
iteratively solves for an increment Δp to update the current
estimation using
Pnew←p+Δp,
(4)
and the expression (3)
can be re-written as the error function
∑x[I(W(x;p+Δp))−T(x)]2.
(5)
The iteration process will continue until the estimate of p
converges, and the converged estimate will serve as the new set of optimized
parameters p for the warp function W(x;p).Furthermore, to linearize the error function (5), the first-order Taylor expansion is used to approximate I(W(x;p + Δp)) as
I(W(x;p+Δp))=I(W(x;p))+∇I∂W∂pΔp
(6)
Therefore, by substituting I(W(x;p + Δp)),
Eq.
(6), into (5),
the error function can be written as [29]
∑x[I(W(x;p))+∇I∂W∂pΔp−T(x)]2,
(7)
where ∇I is the gradient of image I
evaluated when the current warp function W(x;p) is
applied, and ∂W∂p
represents the Jacobian of the warp.The affine transformation W(x;p) can be decomposed as W(x;p) = (Wx, Wy)T, and the Jacobian of W(x;p) is represented as
∂W∂p=⎛⎝⎜⎜⎜∂Wx∂p1∂Wy∂p1⋯⋯∂Wx∂p6∂Wy∂p6⎞⎠⎟⎟⎟=(x00xy00y1001).
Based on Eq.
(8), to minimize the error function (7),
we first determine its partial derivative and set it to zero. Next, the
closed-form solution for the Δp can then be obtained as [29]
Δp=H−1∑x[∇I∂W∂p]T[T(x)−I(W(x;p))].
(9)
In Eq.
(9) H is the n × n Hessian Matrix and the
Gauss–Newton approximation is adopted for Hessian Matrix as
H=∑x[∇I∂W∂p]T[∇I∂W∂p].
(10)
2.3. The weighting function for robust tracking
Constant brightness over the entire image sequence is the primary
assumption for template-based tracking. This assumption is often violated when
the brightness variations are unavoidable, such as the imaging of transient
processes. In our application the assumption can hold for the static IR image
sequence. For the dynamic IR image sequences this assumption will be violated,
since the purpose is to analyze the time-varying thermal signal of a skin
lesion recovering from a cooling excitation.
Therefore, to tackle the problem of brightness variation in dynamic IR image
data, the robust version of Lucas–Kanade tracking algorithm [18,30,31]
is implemented in our application. The robust version adds a weighting mask to
the template pixels in the computation of the least square process. In this
algorithm the pixels with brightness change in the template image will be
treated as outliers, and their contribution will be suppressed in the
computation. Furthermore, to improve the efficiency of the Lucas–Kanade
algorithm, the inverse compositional algorithm [29–31]
is also implemented in the robust version. In the inverse compositional algorithm
the roles of template image T(x) and subsequent image I(x)
are interchanged. Based on the notation of [30,31],
a weighing mask M(x) of the same dimension as the template
image is included in the least square process to replace Eq.
(5)
∑xM(x)⋅[I(W(x;p+Δp))−T(X)]2.
(11)
The closed-form expressions for the increment Δp, described by Eqs.
(9) and (10),
will be replaced by Eqs.
(12) and (13)
in the robust version of Lucas–Kanade tracking algorithm as:
Δp=H‒−1∑xM(x)⋅[∇T∂W∂p]T[I(W(x;p))−T(x)]and
(12)
H‒=∑xM(x)⋅[∇T∂W∂p]T[∇T∂W∂p].
(13)
2.4. Infrared image acquisition
To test the tracking performance of the algorithm, a subject's left hand with deliberate random in-plane motion is imaged using an IR camera. Two types of IR image sequences were tested in this study:- Steady-state IR image sequence: In the first imaging experiment, the temperature of the skin does not change with time, therefore the basic tracking performance can be tested under the constant brightness assumption.
- Thermal recovery IR image sequence: In our previous clinical application [2], the thermal recovery IR image sequence after cooling reveals critical information to detect the malignancy of melanoma lesions. Thus in the second experiment, we first applied cooling to the skin for 60s, and acquired the IR video after the cooling was removed. The temperature changes cause significant changes of brightness in the IR image sequence. Using these test images, the robustness of the tracking algorithm can be tested for the situation when the skin temperature changes with time.
As shown in Fig. 3(a), the camera used in
the experiment is a Merlin midwave (3–5 μm) infrared camera (MWIR) (FLIR
Systems Inc., Wilsonville, OR), which has the temperature sensitivity of 0.025
°C. A 320 × 256 pixel focal plane array (FPA) is used to acquire 16 bit raw
data at frame rate of 60 Hz. Each image frame has the field of view (FOV) of 22
× 16 degrees. Using the parameters obtained from black body calibration [32],
the camera can generate an IR thermal image calibrated as temperature value.
2.5. Template image and the simulated lesion
In general, a skin lesion (early stage melanoma is of particular interest
in the present study) is not directly identifiable in the IR image because the
temperature difference between the lesion and the healthy skin is typically
very small under steady state conditions, i.e. it is of the order of natural
temperature fluctuations of the skin. The temperature difference increases
during dynamic imaging, which imposes the need to track a particular physical
location on the skin throughout an image sequence. To implement the
template-based algorithm and evaluate its performance, we created two paper
markers which are both visible in the IR image (Fig. 3(b) and (c)). One of
them is a square marker of size 5 cm × 5 cm, which serves as the constant
feature in the template image as shown in Fig. 3(c). The second one is a
small, round maker, which is easy to segment in IR image, such that we can
simulate the lesion location and evaluate the tracking results quantitatively.
2.6. Weighting mask for the template image
As introduced in Section 2.3, to implement the robust version of
Lucas–Kanade algorithm [18,29–31],
we used the normalized intensity of the template image as the weighting mask.
In the template image, the image brightness is normalized in the range (0,1)
with respect to the lowest and the highest temperature value. This normalized
image serves as the weighting mask M(x) in Eq.
(11). The weighting mask treats a pixel x as reliable if M(x)
= 1, and a pixel x is considered an outlier if M(x)
= 0. The two weighting masks for the steady-state and thermal recovery IR image
sequences are displayed in Fig. 4a and b. It can be
observed that the skin region outside the square marker, which has higher and
more constant temperature throughout the IR video, will be highly weighted by
the weighting mask (Fig. 4b, light colored
region). On the other hand, the region exposed to cooling in the thermal
recovery template image (Fig. 4b, darker region), which
has lowest temperature but highest brightness variation rate, will be
suppressed as unreliable outlier pixels by the weighting mask. This example
explains the reason why the normalized temperature image is taken as our
weighting mask. The robust Lucas–Kanade algorithm with pixel weighting was
implemented using the public domain MATLAB function shared by Dir-Jan Kroon [33].
(a) Weighting mask of the template image for the
steady-state IR image sequence, (b) weighting mask of the template image for
the thermal recovery IR image sequence (created after the cooling is applied),
(c) the appearance of the template images after ...
2.7. Determining the location of the simulated lesion for tracking error analysis
In order to evaluate and quantify the tracking performance of our
algorithm, we need to know the actual location of the simulated lesion (round
marker) in order to be able to compare it with the estimated location
determined by the motion tracking algorithm. For this reason the simulated
lesion, which is clearly visible in the IR image sequence, is used in the
analysis as opposed to a real (cancerous or benign) skin lesion, which usually
cannot be detected directly in the IR image. The centroid point of the
simulated lesion is taken as the reference coordinate, and it is the mean value
of the pixel coordinates of the marker's border. The coordinates of the border
of the simulated lesion were determined using an outline segmentation algorithm
– a random walker [34].
By selecting two points inside/outside of the closed region of an object, the
random-walker algorithm can automatically segment the object's outline. After
the simulated lesion outline is delineated, its centroid location can be
obtained as the reference point.
When the tracking algorithm is applied, the centroid location (segmented in
the initial frame) will be predicted in the subsequent frames based on the
parameters obtained by solving Eq.
(12). Finally, when knowing both the actual (from image
segmentation) and predicted (from the tracking algorithm) centroid locations in
the image sequence, the Euclidean distance between them is computed to quantify
the tracking error.In Experiment A (steady state), since no cooling is applied to the skin, the entire region of simulated lesion is visually identifiable throughout the IR image sequence. Therefore, the outline of the simulated lesion can be directly segmented in each frame (Fig. 5) using random-walker algorithm.
(a) Adhesive markers and the segmented outline of the
simulated lesion (red circle); (b) Red cross: the centroid location of the
simulated lesion. (For interpretation of the references to colour in this figure
legend, the reader is referred to the web ...
In Experiment B (during thermal recovery) the border of the simulated lesion
is obstructed (masked) by the cooling spot, therefore the region of the
simulated lesion is no longer clearly visible in the IR image (Fig. 6b) and the lesion
outline cannot be segmented directly. Therefore we had to apply registration
between the IR images before and after cooling in order to determine the lesion
outline in the thermal recovery image sequence. To accomplish this, we used the
steady-state image of the simulated lesion before cooling, for which the
random-walker algorithm can be directly applied, to segment the outline. This
lesion outline is then registered to the recovery image sequence via the
location of four corners of the square marker, which are visible in both images
before and after cooling.
Registration of the simulated lesion from the steady-state
IR image (a) to the thermal recovery IR image (b) in Experiment B. The color
change from (a) to (b) within the rectangular template is characteristic for
the cooling process in dynamic IR imaging. ...
As shown in Fig. 6a, the lesion is first
segmented in the image before cooling. Next, by identifying the location of
four corners of the window marker in the images before and after cooling (Fig. 6b), a 2D projective
transformation matrix is solved [35]
based on the four corners correspondences. The identification of the four
corners can be accomplished either manually or automatically using
corner-detecting algorithms such as the Harris detector [36]
or the Shi and Tomasi [37]
method. The registration relation between these two frames can thus be built,
and then the lesion outline segmented in the image before cooling can be mapped
into the image after cooling (Fig. 6b). By applying the
registration process consecutively to individual image frames in a sequence,
the actual location of the lesion centroid can be determined for each frame of
the thermal recovery sequence.
3. Results
3.1. Experiment A: Tracking performance for a steady-state IR image sequence
We applied the algorithm described in Section 2 to 23 frames of
typical steady-state IR images with random in-plane motion incorporated. The
tracking results, represented by 3 pairs of adjacent frames, are shown in Fig. 7. The magnified views of
the predicted (circle) and actual (cross) locations of the simulated lesion are
also shown in Fig. 7. As we compare the
location predicted by the algorithm with the actual location measured by the
segmentation, it can be seen that despite the quite significant random in-plane
motion, the two locations are very close to each other and the deviations are
minimal.
Tracking results for Experiment A in a steady-state image
sequence (circle – location of the centroid predicted by the tracking
algorithm, cross – actual centroid location of the simulated lesion). The
magnified view of the simulated lesion ...
To evaluate the tracking error quantitatively, the Euclidean
distance between the predicted and actual location of the lesion centroid in
units of pixels is calculated for each frame, and the results are plotted as
blue bars in Fig. 8. In addition, the
displacement of the lesion centroid with respect to the previous frame (the
displacement from frame i-1 to i) is also recorded in units of pixels. As
evident from Fig. 8, despite of the fact
that the frame-to-frame displacement can exceed 35 pixels, the tracking errors
over the entire image sequence are smaller than 3 pixels. This result suggests
that the tracking algorithm performs very well even for relatively large
in-plane motion. To translate these data into real-life dimensions, the actual
size of marker is introduced: 30 mm correspond to 86 pixels in the IR image.
Therefore we can infer that 1 pixel approximately corresponds to 0.35 mm on the
skin surface. Using this conversion, the highest tracking errors of 3 pixels
correspond to 1.05 mm in the real dimensions. The displacement of 35 pixels
corresponds to approximately 11 mm motion amplitude in terms of physical
dimensions, which is significant. In medical applications the motion amplitude
of the subject is typically much smaller, of the order of a few millimeters.
The measurement accuracy and spational resolution can be further improved by
improving the spatial resolution of the focal plane array of the camera.
Tracking error analysis for the steady-state image sequence
in experiment A: the red line represents the frame-to-frame displacement (from
frame i-1 to frame i) of the lesion centroid, and the blue bars represent the
Euclidean distance between the predicted ...
3.2. Experiment B-1: Tracking performance for the thermal recovery IR image sequence (initial time interval, within 30 s into thermal recovery)
The tracking performance for the image sequence recovered from
dynamic IR imaging with cooling is illustrated by the 3 pairs of adjacent
frames in Fig. 9. In this trial, the
first 23 IR images after the removal of the cooling excitation are considered.
The frame rate is 1 frame/s, and the quantitative analysis of the tracking
error at each frame is shown in Fig. 10. It can be observed
that despite of the moderate brightness variations during thermal recovery, the
lesion centroid can still be tracked with reasonable accuracy within the first
23 frames. This corresponds to 23 s into the thermal recovery phase, and the
highest tracking errors are smaller than 4 pixels. The results suggest that,
despite of the moderate brightness variations, the template-based algorithm
performs with good accuracy in the first 23 s into the thermal recovery sequence.
This is accomplished by taking the target region in the first frame as the
template image, along with its normalized image, as the weighting mask.
Tracking performance of Experiment B-1 for the first 23
frames in the thermal recovery IR image sequence (circle – centroid location
predicted by the tracking algorithm, cross – actual centroid location of the
simulated lesion).
Tracking error analysis (Experiment B-1) of the thermal
recovery image sequence after the removal of cooling: the red line represents
the frame-to-frame displacement (from frame i-1 to frame i) of the simulated
centroid. The blue bars represent the Euclidean ...
3.3. Experiment B-2: Tracking performance for the thermal recovery IR image sequence (over 30 s into the thermal recovery phase) without template update
As we extended the tracking test over 60 frames (1 min) in the
second trial, the tracking performance of the algorithm described in Section
3.2 deteriorated. The four frames shown in Fig. 11a illustrate the
dramatic changes the IR image undergoes during thermal recovery. As a
consequence of these changes, the tracking algorithm gradually loses the track
of simulated lesion between frames 41 and 65. The error analysis in Fig. 11b indicates a dramatic
increase of tracking errors after frame 29. The significant error growth can be
attributed to the brightness inconsistency after 30 s into the thermal recovery
phase, when the thermal appearance of the cooled region becomes substantially
different from the template image recorded at the first frame. As indicated in Fig. 11b, the tracking errors
exceed 5 pixels after frame 41. Therefore, to ensure the robustness the
tracking method, adjustments to the template-based algorithm were needed. These
adjustments are expected to deal with the brightness inconsistencies
encountered during the thermal recovery.
Tracking performance for Experiment B-2 with tracking
duration of 90 s into the thermal recovery phase without template update: (a)
four representative image frames illustrating the differences between centroid
location predicted by the algorithm and ...
3.4. Experiment B-3: Tracking performance of the thermal recovery IR image sequence with template update for long imaging times
To reduce the growth of tracking errors due to brightness
inconsistency after 30 s into the thermal recovery, instead of a using single
template image taken at the initial time, we update the template image and the
weighting mask at frame 29 as illustrated in Fig. 12. Frame 29 is chosen
because it is the last frame with errors smaller than 5 pixels, before
significant error growth is observed in Fig. 11b. This image frame has
similar brightness as the rest of the frames recorded after 30 s. Thus the
tracking procedure for longer imaging times has two stages: in the first stage
the images from frames 1–29 are tracked using the initial template image. In
the second stage, after the update at frame 29, the remaining frames are
tracked using the updated template image (Fig. 12).
(a) Updated template image, frame 29, (b) updated weighting
mask for frame 29 and (c) resulting temperature value for the updated template
image after applying the weighting mask.
The improvements achieved with the template update incorporated into
the tracking algorithm are presented in Fig. 13. In Fig. 13a, we can observe that
the simulated lesion can be tracked correctly in the image after frame 30. The
improved accuracy is obvious when compared with its counterpart shown in Fig. 11a (without template
update). The tracking error (blue bars) and displacement magnitude (red line)
charts for the experiment using template update are shown in Fig. 13b. When compared with
the data in Fig. 11b, it is evident that
the tracking errors are significantly reduced, under 5 pixels, throughout the
image sequence. The results suggest that, when applying the tracking algorithm
to the thermal recovery image sequence, updating the template information at
one (or more) selected time instant(s) can improve the tracking robustness for
longer imaging times despite the brightness inconsistencies caused by cooling.
Tracking performance of Experiment B-3 for 90 image frames
with a template update carried out at frame 29: (a) Four representative frames
showing the location differences between the predicted and actual lesion
centroid and (b) the tracking errors at ...
4. Conclusions
In this study, we demonstrated the feasibility of template-based
algorithm for involuntary motion tracking in in-vivo infrared imaging. In the
presence of random in-plane motion, we demonstrated that the robust version of
Lucas–Kanade algorithm can track the location of the target region in IR images
sequences with good accuracy for sequences with small temperature changes
(steady state). When applying the algorithm to the steady-state IR image
sequence, satisfactory results were obtained when a single template information
is used, detected at the first frame.
As discussed for Experiment B-1, the tracking scheme using a single template
image can also achieve accurate tracking results during the first 30 s into the
thermal recovery image sequence. The results suggest that the weighting mask,
created by normalizing the template image at the initial frame, can effectively
ensure the tracking robustness in the early stage of the thermal recovery phase
(0–40 s). During this period the temporal change of skin temperature is highest
and the thermal signature of lesion most pronounced [38].As the duration of the thermal recovery exceeds 30 s, at later times the brightness of the cooled region differs significantly from the initial template image, which causes the tracking errors to grow significantly, as demonstrated in Experiment B-2.
From the improved tracking results obtained in Experiment B-3, we
can infer that by updating the template information at the end of the early
stage (around 30 s), tracking robustness can be ensured throughout a longer
image sequence. The approach reveals that in dynamic IR imaging applications
the template update can be an effective amendment to stabilize and improve the
tracking performance of the template-based algorithm.
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