Please use this identifier to cite or link to this item: http://hdl.handle.net/2080/3681
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dc.contributor.authorMishra, Vartika-
dc.contributor.authorRath, Subhendu-
dc.contributor.authorRath, Santanu Kumar-
dc.date.accessioned2022-06-01T10:29:42Z-
dc.date.available2022-06-01T10:29:42Z-
dc.date.issued2022-03-
dc.identifier.citation4th International Conference on Machine Intelligence and Signal Processing (MISP), NIT Raipur, 12-140March 2022en_US
dc.identifier.urihttp://hdl.handle.net/2080/3681-
dc.descriptionCopyright belongs to proceeding publisheren_US
dc.description.abstractIn a recent study, to detect breast cancer abnormalities, thermography has been observed to be a qualitative modality. Due to an increase in blood vessel activity, the cancer cells and the tissues become hotter. Thus, by using thermography, the thermograms of the image are captured which depict the surface temperature of the breast, where the hot spot is indicated by the higher temperature. This modality has increased the non-invasiveness in detecting breast abnormalities. The different channels of the RGB (Red, Green, and Blue) image gives information of the different intensity of the color with respect to the image. In this work, the breast thermograms corresponding to the red channel are extracted for analysis. Further, three different thresholding methods viz., Otsu thresholding, Adaptive Mean Thresholding, and Adaptive Gaussian Thresholding methods are applied which depicts the local image characteristics of the image. The image enhancement methods improve the quality of the image. Further, the statistical features are extracted from the obtained thresholded images, and two different classifiers Random Forest and Decision Tree are applied for classifying the normal and abnormal breasten_US
dc.subjectThermographyen_US
dc.subjectcolor channelsen_US
dc.subjectthresholdingen_US
dc.subjectfeature selectionen_US
dc.titleLocal and Global thresholding-based breast cancer detection using thermogramsen_US
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