Thermography based breast cancer analysis using statistical features and fuzzy classification

Schaefer, Gerald; Zavisek, M.; Nakashima, Tomoharu

doi:10.1016/j.patcog.2008.08.007

Cited by 173 publications

(77 citation statements)

References 12 publications

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“…Thus, thermography has become an important indicator of abnormalities, since the asymmetrical distribution of temperature, with the presence of cold and hot areas, is strongly suggestive of disorders. 43 Infrared thermography has been described in the literature as an assessment tool for individuals with different adverse health conditions, such as carpal tunnel syndrome, 44 breast cancer, 45 circulatory changes, 46 myofascial disorders 31 and TMD. 17,18 This painless, noninvasive, non-ionizing method does not require contact with the region being evaluated, thereby offering safety and comfort to the patient.…”

Section: Discussionmentioning

confidence: 99%

Influence of Phototherapy on Thermographic Images and Pain in Individuals with Temporomandibular Disorder: Protocol for a Randomized, Placebo-Controlled, Double-Blind Clinical Trial

Tosato¹,

Herpich²,

Leal-Junior³

et al. 2015

Dent Open J

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Section: Discussionmentioning

confidence: 99%

Influence of Phototherapy on Thermographic Images and Pain in Individuals with Temporomandibular Disorder: Protocol for a Randomized, Placebo-Controlled, Double-Blind Clinical Trial

Tosato¹,

Herpich²,

Leal-Junior³

et al. 2015

Dent Open J

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“…This temperature increase is caused by the high metabolic activity of cancer cells [142]. IR imaging has frequently been used in clinics to detect changes in skin surface temperature associated with some superficial tumors [143].…”

Section: Cancermentioning

confidence: 99%

“…Therefore, in normal breast thermograms, symmetric heat patterns are observed in both breasts, but in the case of an unilateral abnormality, asymmetry is observed. Thermography is especially well suited for picking up tumors in their early stages or tumors in dense tissue and in these scenarios it outperforms other modalities, such as mammography [142]. Figure 4 shows normal (Figure 4a) and malignant (Figure 4b) thermography images of the female breast.…”

Section: Breast Cancermentioning

confidence: 99%

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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“…Table 1 shows the related studies done in breast cancer detection using thermography techniques. [11][12][13][14][15][16][17][18][19][20][21][22] In the present study due to a lot of attractions of breast thermography, a method with a very noticeable accuracy, sensitivity and verity has been proposed and then a user friendly tool box developed to assist physicians in early clinical detection of breast cancer based on different degrees of thermal images. First the region of interest (ROI) is determined using full automatic operation and the quality of image is improved.…”

Section: Introductionmentioning

confidence: 99%

Developing a Toolbox for Clinical Preliminary Breast Cancer Detection in Different views of Thermogram Images using a Set of Optimal Supervised Classifiers

Lashkari

Firouzmand

2017

Scientia Iranica

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A full automatic technique and a user friendly toolbox developed to assist physicians in early clinical detection of breast cancer. Database contains different degrees of thermal images obtained from normal or cancerous mammary tissues of patients with mean age of 42.3 years (SD: ±10.50) which their sympathetic nervous system activated with a cold stimulus on hands. First ROI was determined using full automatic operation and the quality of image improved. Then, some features, including statistical, morphological, frequency domain, histogram and GLCM features were extracted from segmented right and left breast. Subsequently, to achieve the best feature space decreasing complexity and increasing accuracy, feature selectors such as mRMR, SFS, SBS, SFFS, SFBS and GA have been used. Finally to classify and TH labeling, supervised learning techniques such as AdaBoost, SVM, kNN, NB and PNN were applied and compared with each other to find the best suitable one. The experimental results obtained on native database showed the mean accuracy of 88.03% for 0 degree images using combination of mRMR and AdaBoost and for combined 3 degrees using combination of GA and AdaBoost. The maximum accuracy obtained from all degrees and their combinations in before and after ice test is nearly to 100%.

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Thermography based breast cancer analysis using statistical features and fuzzy classification

Cited by 173 publications

References 12 publications

Influence of Phototherapy on Thermographic Images and Pain in Individuals with Temporomandibular Disorder: Protocol for a Randomized, Placebo-Controlled, Double-Blind Clinical Trial

Influence of Phototherapy on Thermographic Images and Pain in Individuals with Temporomandibular Disorder: Protocol for a Randomized, Placebo-Controlled, Double-Blind Clinical Trial

Application of infrared thermography in computer aided diagnosis

Developing a Toolbox for Clinical Preliminary Breast Cancer Detection in Different views of Thermogram Images using a Set of Optimal Supervised Classifiers

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