A novel functional infrared imaging system coupled with multiparametric computerised analysis for risk assessment of breast cancer

Sella, Tamar; Sklair‐Levy, Miri; Cohen, Maya; Rozin, M; Shapiro-Feinberg, Myra; Allweis, Tanir; Libson, Eugene; Izhaky, David

doi:10.1007/s00330-012-2724-7

Cited by 17 publications

(12 citation statements)

References 34 publications

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“…This surface separates the datapoints, which belong to distinct classes, from each other with maximum margin. SVMs are popular, because of their high classification accuracy even under adverse conditions, such as nonlinear and high dimensional datasets and very few cases for training the model [66,67]. If the data is nonlinear, the boundary problems, encountered by the SVM, are nonlinear as well [68,69].…”

Section: Support Vector Machine (Svm)mentioning

confidence: 99%

“…Receiver Operating Characteristic (ROC) plots provide a pure index of accuracy by demonstrating the limits of a test's ability to discriminate between alternative states of health over the complete spectrum of operating conditions. A wide range of studies on IR thermography used the ROC curve to measure the cutoff point, diagnostic accuracy (indicated by the area under the curve), sensitivity, and specificity [67,89]. Despite these positive characteristics, Cook points out that ROC measures may be mediocre in assessing models that predict future risk or stratify individuals into risk categories [90].…”

Section: Receiver Operating Characteristic (Roc)mentioning

confidence: 99%

“…A temperature value above the threshold indicates disease and a value below the threshold indicates a normal sample. More advanced analysis is done using ROC and Area Under Curve (AUC) [67,88,94]. However, both methods are based on scalar thesholding as well.…”

Section: Applicationsmentioning

confidence: 99%

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Application of infrared thermography in computer aided diagnosis

Faust

Acharya

et al. 2014

Infrared Physics & Technology

100

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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 nonlinear. 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 ComputerAided 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.

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Section: Support Vector Machine (Svm)mentioning

confidence: 99%

Section: Receiver Operating Characteristic (Roc)mentioning

confidence: 99%

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Application of infrared thermography in computer aided diagnosis

Faust

Acharya

et al. 2014

Infrared Physics & Technology

100

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“…In a previous proof of concept study, three-dimensional functional infrared imaging (3DIRI) showed high sensitivity in assessing the likelihood of breast cancer [14] and may be a useful addition to the arsenal of tools in breast cancer diagnosis. This method, based on physiology and metabolic changes, is non-invasive, does not involve ionizing radiation, involves no contact with the breasts, and is thought to operate independent of breast density.…”

Section: Introductionmentioning

confidence: 99%

Does three-dimensional functional infrared imaging improve breast cancer detection based on digital mammography in women with dense breasts?

et al. 2019

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PurposeWe aimed to estimate the incremental cancer detection rate achieved by adding three-dimensional functional infrared imaging (3DIRI) to digital mammography in women with dense breasts.Materials and methodsIn this prospective study conducted between December 2014 and April 2016, 1727 women (median age 56) with percentage volumetric breast density > 6% were recruited at routine screening mammography to undergo additional 3DIRI. The 3DIRI findings were classified as negative or positive. Women with a negative mammography but positive 3DIRI were referred to dynamic contrast-enhanced MRI, whereas all other women underwent routine follow-up based on the mammography finding. Diagnosis of breast cancer was verified by histopathologic examination. The number of women diagnosed with a malignancy formed the basis of our statistical analysis.ResultsMammography detected 7 cancers in 7 women. Of 1692 women with negative mammography, 222 women (13%) had a positive 3DIRI of which 219 underwent MRI. An additional 6 cancers were identified in 5 women, increasing the diagnostic yield from 7 of 1727 (0.41%) to 12 of 1727 (0.69%). The incremental cancer detection rate associated with using 3DIRI to select women for MRI was 5 of 222 (22.5 additional cancers per 1000).ConclusionThe use of 3DIRI to select women for an additional MRI can result in the detection of additional cancers in women with dense breasts, but at the expense of additional false positives and considerably lower positive predictive value of the combined examinations. Additional studies are necessary to evaluate the role of 3DIRI as an adjunct to mammography.Key Points• Use of three-dimensional functional infrared imaging to select women for an MRI in addition to screening mammography has the potential to improve breast cancer detection in women with dense breasts.Electronic supplementary materialThe online version of this article (10.1007/s00330-019-06248-y) contains supplementary material, which is available to authorized users.

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“…Positively, SVM can yield significant accuracy particularly in difficult conditions. This include high dimensional and nonlinear datasets and, little cases to train the model [145,146]. For a nonlinear data, the boundary problem is also nonlinear.…”

Section: Support Vector Machine (Svm)mentioning

confidence: 99%

Detection of diabetic foot with infrared thermography

Rahim¹

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Diabetic foot is among the major complications of Diabetes Mellitus (DM) that can results in ulcerations. An early detection and appropriate treatment can prevent traumatic outcomes such as lower extremity amputation. Furthermore, studies have shown that temperature fluctuations on the plantar foot can be related to diabetic foot complications. Since the time of Hippocrates, human body temperature is being associated to health. Thermography is a non-invasive imaging method employed to view the thermal patterns using Infrared (IR) camera. In addition, it allows the qualitative and visual documentation of temperature fluctuation in vascular tissues. Absolutely, IR thermography can play a crucial role in the medical field, especially for detection of diabetic foot disease. Nonetheless, such strategy is yet to be established and many circumstances still unsolved. The first challenge is analysing the thermal changes because the risks of ulceration are associated to the rise in plantar temperatures. Nevertheless, the attempts to identify the spatial patterns of temperature remains difficult because there are extensive forms of thermal patterns. This complicates the classification process. In addition, the interpretation of thermographic patterns may be difficult due to inadequate details, even in healthy subjects, and classification techniques on the thermographic patterns. Moreover, there is no objective approach in allocating the thermogram to certain classes. Recent studies proposed the temperatures of one foot and contralateral foot to be measured and compared, and the diabetic foot risk regions are detected by the defined threshold. However, this is only applicable in without ulceration or amputation cases and in asymmetric thermal changes. III Therefore, this research developed a novel computer aided detection (CAD) system for diabetic foot using nonlinear features extracted from plantar foot thermograms. A total of 33 healthy volunteers and 33 non-neuropathy diabetic patients are recruited from Ngee Ann Polytechnic (NPIRB-P0175-2017-ECE-AMA6) and Singapore General Hospital (SGH), Diabetes & Metabolism Center (DMC) (CIRB Ref: 2016/3044) respectively. The thermograms acquired are pre-processed by segmenting the plantar foot regions using polygon and then proceed to warp all the segmented plantar foot regions into uniform size. Afterward, the warped grayscale foot images are decomposed using Discrete Wavelet Transform (DWT) and Higher Order Spectra (HOS) prior to extracting texture and entropy features. The features values from left and right foot are subtracted. Subsequently, student t-test is applied on the resultant features to select and rank the significant features. Lastly, the 27 significantly ranked features (p value < 0.0001) are fed independently into support vector machine (SVM) classifier. The developed methodology achieved maximum classification accuracy of 89.39%, positive predictive value of 96.43%, sensitivity of 81.81% and specificity of 96.97% using only five features. The findings of this researc...

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A novel functional infrared imaging system coupled with multiparametric computerised analysis for risk assessment of breast cancer

Cited by 17 publications

References 34 publications

Application of infrared thermography in computer aided diagnosis

Application of infrared thermography in computer aided diagnosis

Does three-dimensional functional infrared imaging improve breast cancer detection based on digital mammography in women with dense breasts?

Detection of diabetic foot with infrared thermography

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