Diagnosis of breast cancer in light microscopic and mammographic images textures using relative entropy via kernel estimation.
The aim of this article was to provide early detection of breast cancer by using both mammography and histopathology images of the same patient. When the studies in the literature are examined, it is seen that the mammography and histopathology images of the same patient are not used together for early diagnosis of breast cancer. Mammographic and microscopic images can be limited when using only one of them for the early detection of the breast cancer. Therefore, multi-modality solutions that give more accuracy results than single solutions have been realized in this paper. 3 × 50 microscopic (histopathology) and 3 × 50 mammography image sets have been taken from Firat University Medicine Faculty Pathology and Radiology Laboratories, respectively. Optimum feature space has been obtained by minimum redundancy and maximum relevance via mutual information method applying to the 3 × 50 microscopic and mammography images. Then, probabilistic values of suspicious lesions in the image for selected features have been found by exponential curve fitting. Jensen Shannon, Hellinger, and Triangle measurements have been used for the diagnosis of breast cancer. It has been proved that these measures have been related to each other. Weight values for selected each feature have been found using these measures. These weight values have been used in object function. Afterward, histopathology and mammography images have been classified as normal, malign, and benign utilizing object function. In the result of this classifier, the accuracy of diagnosis of breast cancer has been estimated probabilistically. Furthermore, classifications have been probabilistically visualized on a pie chart. Consequently, the performances of Jensen Shannon, Hellinger, and Triangle measures have been compared with ROC analysis using histopathology and mammography test images. It has been observed that Jensen Shannon measure has higher performance than Hellinger and Triangle measures. Accuracy rates of histopathology and mammography images in Jensen Shannon measure have been found to 99 and 98%, respectively.
Department of Electrical-Electronic Engineering, Engineering Faculty, Firat University, 23100, Elazig, Turkey. sevcanaytackorkmaz@gmail.com.
Sevcan Aytac
SA
Korkmaz
Department of General Surgery, Medicine Faculty, Firat University, 23100, Elazig, Turkey.
Mehmet Fatih
MF
Korkmaz
Department of Electrical-Electronic Engineering, Engineering Faculty, Firat University, 23100, Elazig, Turkey.
Mustafa
M
Poyraz
D000465:Algorithms; D001943:Breast Neoplasms / Q000175:diagnosis* / Q000473:pathology; D019277:Entropy; D005260:Female; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008327:Mammography / Q000379:methods*; D008853:Microscopy / Q000379:methods*; D011336:Probability
Breast histopathology images; Feature selection; Hellinger measure; Jensen Shannon divergence; Mammography; Mutual information; Triangle distance