Fractal feature based early breast abnormality prediction

Roy, Anindita; Gogoi, Usha Rani; Das, Dipak; Bhowmik, Mrinal Kanti

doi:10.1109/r10-htc.2017.8288896

Cited by 6 publications

(3 citation statements)

References 11 publications

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“…Roy et al 20 proposed a breast cancer detection approach by combining the mammography and thermography based on the fractal features of abnormal regions of the breast. Their combined fractal approach analyzed using the mini‐MIAS mammogram dataset has achieved the prediction accuracy of about 95.94% and seemed to be the most efficient with texture features 79.31% and 78.94% detected the affected breast region.…”

Section: Literature Reviewmentioning

confidence: 99%

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Thermography based breast cancer detection using self‐adaptive gray level histogram equalization color enhancement method

Raj¹,

Sundaram²,

Jaya³

2020

Int J Imaging Syst Tech

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The early detection of tumor is necessary to save a number of lives. In women, the temperature of the affected area of the tumor is warmer than the unaffected area; therefore the thermography technique can be used to capture the cancerous breast images with a thermal infrared by identifying the temperature difference between them. Color enhancement of the captured breast image is an important consideration for early detection of tumor at this stage. Therefore, in this paper, we propose a self‐adaptive gray level histogram equalization approach to enhance the color of the IR image for early detection of the tumor. This approach does not require any manual feeding of parameters toward images. The final classification of tumorous and non‐tumorous breast images can obtain through certain procedures, which includes, image acquisition, pre‐processing, segmentation, feature extraction and classification. This paper emphasizes the support vector machine (SVM) technique to classify the tumor IR thermography images. The proposed approach is implemented in MATLAB and the experimental results shows an outstanding color enhancement of IR images and better classification compared to other existing methods such as CLAHE, BIi‐histogram equalization and adaptive histogram equalization. The performance was evaluated by using evaluation metrics such as sensitivity, accuracy, and specificity of thermography breast image by the SVM classifier adapted with various color enhancement approaches are found to be 91.6%, 90%, and 87.5%. This approach helps in medical field for early diagnosis with high reliability.

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Section: Literature Reviewmentioning

confidence: 99%

“…The spikes and noise from the original histogram can be removed by using the following modified histogram equalization is given in Equation (20),…”

Section: Modified Histogram Equalizationmentioning

confidence: 99%

Thermography based breast cancer detection using self‐adaptive gray level histogram equalization color enhancement method

Raj¹,

Sundaram²,

Jaya³

2020

Int J Imaging Syst Tech

View full text Add to dashboard Cite

show abstract

“…Thermograms have played a significant role in diagnostic imaging studies. They serve as a source for developing image processing activities, which include segmentation tasks for separating the regions under study, as presented in [ [5] , [6] , [7] , [8] , [9] , [10] ] and identification of descriptive features and classification, as published [ [11] , [12] , [13] , [14] , [15] , [16] , [17] ]. Although most studies reported before 2014 were conducted without public access images sets that prevented reproducibility and comparison of results, it was until 2014 that the first public database of breast thermographic images was presented.…”

Section: Introductionmentioning

confidence: 99%