Breast cancer detection using infrared thermal imaging and a deep learning model

Women’s breasts are susceptible to developing cancer; this is supported by a recent study from 2016 showing that 2.8 million women worldwide had already been diagnosed with breast cancer that year. The medical care of a patient with breast cancer is costly and, given the cost and value of the preservation of the health of the citizen, the prevention of breast cancer has become a priority in public health. Over the past 20 years several techniques have been proposed for this purpose, such as mammography, which is frequently used for breast cancer diagnosis. However, false positives of mammography can occur in which the patient is diagnosed positive by another technique. Additionally, the potential side effects of using mammography may encourage patients and physicians to look for other diagnostic techniques. Our review of the literature first explored infrared digital imaging, which assumes that a basic thermal comparison between a healthy breast and a breast with cancer always shows an increase in thermal activity in the precancerous tissues and the areas surrounding developing breast cancer. Furthermore, through our research, we realized that a Computer-Aided Diagnostic (CAD) undertaken through infrared image processing could not be achieved without a model such as the well-known hemispheric model. The novel contribution of this paper is the production of a comparative study of several breast cancer detection techniques using powerful computer vision techniques and deep learning models

Autonomous robotic system for thermographic detection of defects in upper layers of carbon fiber reinforced polymers

Carbon Fiber Reinforced Polymers (CFRPs) are composites whose interesting properties, like high strength-to-weight ratio and rigidity, are of interest in many industrial fields. Many defects affecting their production process are due to the wrong distribution of the thermosetting polymer in the upper layers. In this work, they are effectively and efficiently detected by automatically analyzing the thermographic images obtained by Pulsed Phase Thermography (PPT) and comparing them with a defect-free reference. The flash lamp and infrared camera needed by PPT are mounted on an industrial robot so that surfaces of CFRP automotive components, car side blades in our case, can be inspected in a series of static tests. The thermographic image analysis is based on local contrast adjustment via UnSharp Masking (USM) and takes also advantage of the high level of knowledge of the entire system provided by the calibration procedures. This system could replace manual inspection leading to a substantial increase in efficiency

Preoperative Systems for Computer Aided Diagnosis based on Image Registration: Applications to Breast Cancer and Atherosclerosis

Computer Aided Diagnosis (CAD) systems assist clinicians including radiologists and cardiologists to detect abnormalities and highlight conspicuous possible disease. Implementing a pre-operative CAD system contains a framework that accepts related technical as well as clinical parameters as input by analyzing the predefined method and demonstrates the prospective output. In this work we developed the Computer Aided Diagnostic System for biomedical imaging analysis of two applications on Breast Cancer and Atherosclerosis. The aim of the first CAD application is to optimize the registration strategy specifically for Breast Dynamic Infrared Imaging and to make it user-independent. Base on the fact that automated motion reduction in dynamic infrared imaging is on demand in clinical applications, since movement disarranges time-temperature series of each pixel, thus originating thermal artifacts that might bias the clinical decision. All previously proposed registration methods are feature based algorithms requiring manual intervention. We implemented and evaluated 3 different 3D time-series registration methods: 1. Linear affine, 2. Non-linear Bspline, 3. Demons applied to 12 datasets of healthy breast thermal images. The results are evaluated through normalized mutual information with average values of 0.70±0.03, 0.74±0.03 and 0.81±0.09 (out of 1) for Affine, BSpline and Demons registration, respectively, as well as breast boundary overlap and Jacobian determinant of the deformation field. The statistical analysis of the results showed that symmetric diffeomorphic Demons registration method outperforms also with the best breast alignment and non-negative Jacobian values which guarantee image similarity and anatomical consistency of the transformation, due to homologous forces enforcing the pixel geometric disparities to be shortened on all the frames. We propose Demons registration as an effective technique for time-series dynamic infrared registration, to stabilize the local temperature oscillation. The aim of the second implemented CAD application is to assess contribution of calcification in plaque vulnerability and wall rupture and to find its maximum resistance before break in image-based models of carotid artery stenting. The role of calcification inside fibroatheroma during carotid artery stenting operation is controversial in which cardiologists face two major problems during the placement: (i) “plaque protrusion” (i.e. elastic fibrous caps containing early calcifications that penetrate inside the stent); (ii) “plaque vulnerability” (i.e. stiff plaques with advanced calcifications that break the arterial wall or stent). Finite Element Analysis was used to simulate the balloon and stent expansion as a preoperative patient-specific virtual framework. A nonlinear static structural analysis was performed on 20 patients acquired using in vivo MDCT angiography. The Agatston Calcium score was obtained for each patient and subject-specific local Elastic Modulus (EM) was calculated. The in silico results showed that by imposing average ultimate external load of 1.1MPa and 2.3MPa on balloon and stent respectively, average ultimate stress of 55.7±41.2kPa and 171±41.2kPa are obtained on calcifications. The study reveals that a significant positive correlation (R=0.85, p<0.0001) exists on stent expansion between EM of calcification and ultimate stress as well as Plaque Wall Stress (PWS) (R=0.92, p<0.0001), comparing to Ca score that showed insignificant associations with ultimate stress (R=0.44, p=0.057) and PWS (R=0.38, p=0.103), suggesting minor impact of Ca score in plaque rupture. These average data are in good agreement with results obtained by other research groups and we believe this approach enriches the arsenal of tools available for pre-operative prediction of carotid artery stenting procedure in the presence of calcified plaques

Application of infrared thermography in computer aided diagnosis

The invention of thermography, in the 1950s, posed a formidable problem to the research community: What is the relationship between disease and heat radiation captured with Infrared (IR) cameras? The research community responded with a continuous effort to find this crucial relationship. This effort was aided by advances in processing techniques, improved sensitivity and spatial resolution of thermal sensors. However, despite this progress fundamental issues with this imaging modality still remain. The main problem is that the link between disease and heat radiation is complex and in many cases even non-linear. Furthermore, the change in heat radiation as well as the change in radiation pattern, which indicate disease, is minute. On a technical level, this poses high requirements on image capturing and processing. On a more abstract level, these problems lead to inter-observer variability and on an even more abstract level they lead to a lack of trust in this imaging modality. In this review, we adopt the position that these problems can only be solved through a strict application of scientific principles and objective performance assessment. Computing machinery is inherently objective; this helps us to apply scientific principles in a transparent way and to assess the performance results. As a consequence, we aim to promote thermography based Computer-Aided Diagnosis (CAD) systems. Another benefit of CAD systems comes from the fact that the diagnostic accuracy is linked to the capability of the computing machinery and, in general, computers become ever more potent. We predict that a pervasive application of computers and networking technology in medicine will help us to overcome the shortcomings of any single imaging modality and this will pave the way for integrated health care systems which maximize the quality of patient care

Thermal Characterization of Infrared Images for Breast Cancer Detection

One of the most common diseases affecting women is breast cancer. As a result, diagnosis and monitoring of breast health is viewed as a lifesaving procedure. Thermography shows promise for diagnosing breast cancer especially for dense breasts on which mammography does not work well. It is a noninvasive and inexpensive method based on the principle that malignant tumors have a higher metabolic rate and therefore emit more heat. A new segmentation algorithm was developed for a recent database of thermal images obtained from patients lying in the prone position. The algorithm was successful in isolating the breast from rest of the body and background. The resulting segmentation was then analyzed using temperature profiles, where significant peaks in the profile indicated a region of interest. Lastly, approximate hot spots were found that indicated the location of a possible tumor

Comparative Evaluation of Medical Thermal Image Enhancement Techniques for Breast Cancer Detection

Thermography is a potential medical imaging modality due to its capability in providing additional physiological information. Medical thermal images obtained from infrared thermography systems incorporate valuable temperature properties and profiles, which could indicate underlying abnormalities. The quality of thermal images is often degraded due to noise, which affects the measurement processes in medical imaging. Contrast stretching and image filtering techniques are normally adopted in medical image enhancement processes. In this study, a comparative evaluation of contrast stretching and image filtering on individual channels of true color thermal images was conducted. Their individual performances were quantitatively measured using mean square error (MSE) and peak signal to noise ratio (PSNR). The results obtained showed that contrast stretching altered the temperature profile of the original image while image filtering appeared to enhance the original image with no changes in its profile. Further measurement of both MSE and PSNR showed that the Wiener filtering method outperformed other filters with an average MSE value of 0.0045 and PSNR value of 78.739 dB. Various segmentation methods applied to both filtered and contrast stretched images proved that the filtering method is preferable for in-depth analysis

Beyond mammography : an evaluation of complementary modalities in breast imaging

Breast cancer is the main cause of cancer death among women worldwide and the goal of mammography screening is to reduce breast cancer-specific mortality. The reduction of the sensitivity of mammography for detecting cancer among women with dense breasts requires the use of complementary methods for this subset of women. Three of the projects in this thesis examine the performance of such complementary methods and a fourth study investigates the association between the biomarker BPE (background parenchymal enhancement) and risk factors for breast cancer. In study 1, we prospectively compared the sensitivity and specificity of Automated Breast Volume Scanner (ABVS) with handheld ultrasound for detection of breast cancer among women with a suspicious mammographic finding who were recalled after attending the population-based mammography screening program. We performed both methods on 113 women and found 26 malignant lesions. Analysis was performed in two categories: breasts with a suspicious screening mammography and breasts with a negative screening mammography. In the first category (n=118) the sensitivity of both methods was 88% (p=1.0), the specificity of handheld ultrasound was 93.5 % and ABVS was 89.2%. The difference in specificity was not statistically significant (p=0.29). For breasts without a suspicious mammographic finding, the sensitivity of handheld ultrasound and ABVS was 100% (p=1.0), the specificity was 100% and 94.1% respectively. The difference in specificity was statistically significant (p=0.03). In summary, ABVS has similar sensitivity to handheld ultrasound, but lower specificity in breasts with a negative mammogram. In study 2, we explored the incremental cancer detection rate when adding a threedimensional infrared imaging (3DIRI) score to screening mammography among women with dense breasts (Volpara volumetric density >6 % on the previous mammography examination) who attended the population-based mammography screening program. Women with a negative mammogram and positive 3DIRI score were triaged for a DCEMRI examination to verify the presence of cancer. Of 1727 participants, 7 women had a mammography-detected breast cancer. Among women with a negative mammogram and a positive infrared imaging (n=219), an additional 6 cancers in 5 women were detected on MRI resulting in an incremental cancer detection rate of 22.5 per 1000. Among women with a negative mammography and infrared examination, one woman was diagnosed with breast cancer during the two-year follow-up. The study does not provide information on the proportion of cancers that might have been detected had MRI been performed among women with a negative mammogram and 3DIRI score. Consequently, this study does not shed light on the diagnostic accuracy of infrared imaging or whether using an infrared risk score is the optimal method for identifying women who would benefit from additional imaging modalities. In study 3, we used MRI examinations of study 2 among women without breast cancer (n=214) to explore the association between BPE at DCE-MRI and a large array of risk factors for breast cancer. Thanks to the Karma database, we had unique access to data from self-reporting questionnaires on risk factors. BPE and mammographic density were assessed visually by three radiologists and BPE was further dichotomized into low and high. We created categorical variables for other risk factors. We calculated the univariable associations between BPE and each risk factor and fitted an adjusted logistic regression model. In the adjusted model, we found a negative association with age (p=0.002), and a positive association with BMI (p=0.03). There was a statistically significant association with systemic progesterone (p=0.03) but since only five participants used progesterone preparations, the result is uncertain. Although the likelihood for high BPE increased with increase in mammographic density, the association was not statistically significant (p=0.23). We were able to confirm earlier findings that BPE is associated with age, BMI and progesterone, but we could not find an association with other risk factors for breast cancer. In study 4, we compared the diagnostic accuracy, reading-time, and inter-rater agreement of an abbreviated protocol (aMRI) to the routine full protocol (fMRI) of contrast-enhanced breast MRI. The MRI examinations were performed before biopsy and among women who were not part of a surveillance program due to an increased familial risk of breast cancer. Analysis was performed on a per breast basis. Aggregated across three readers, the sensitivity and specificity were 93.0% and 91.7% for aMRI, and 92.0% and 94.3% for the fMRI. Using a generalized estimating equations approach to compare the two protocols, the difference in sensitivity was not statistically significant (p=0.840), and the difference in specificity was significant (p=0.003). There was a statistically significant difference in average reading time of 67 seconds for aMRI and 126 seconds for the fMRI (p= 0.000). The inter-rater agreement was 0.79 for aMRI and 0.83 for fMRI. We were able to demonstrate that the abbreviated protocol has similar sensitivity to the full protocol even if MRI is performed before biopsy and the images lack telltale signs of malignancy. In conclusion, this thesis provides new knowledge about the biomarker BPE, broadens our knowledge on the diagnostic accuracy of two different imaging modalities and highlights the importance of good study design for diagnostic accuracy studies

Comparative evaluation of medical thermal image enhancement techniques for breast cancer detection

Thermography is a potential medical imaging modality due to its capability in providing additional physiological information. Medical thermal images obtained from infrared thermography systems incorporate valuable temperature properties and profiles, which could indicate underlying abnormalities. The quality of thermal images is often degraded due to noise, which affects the measurement processes in medical imaging. Contrast stretching and image filtering techniques are normally adopted in medical image enhancement processes. In this study, a comparative evaluation of contrast stretching and image filtering on individual channels of true color thermal images was conducted. Their individual performances were quantitatively measured using mean square error (MSE) and peak signal to noise ratio (PSNR). The results obtained showed that contrast stretching altered the temperature profile of the original image while image filtering appeared to enhance the original image with no changes in its profile. Further measurement of both MSE and PSNR showed that the Wiener filtering method outperformed other filters with an average MSE value of 0.0045 and PSNR value of 78.739 dB. Various segmentation methods applied to both filtered and contrast stretched images proved that the filtering method is preferable for in-depth analysis