Diagnosis of breast cancer in light microscopic and mammographic images textures using relative entropy via kernel estimation

Medical & Biological Engineering & Computing
S Aytac KorkmazM Poyraz

Abstract

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 val...Continue Reading

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Citations

Sep 12, 2017·Medical & Biological Engineering & Computing·Amin Zadeh ShiraziZahra Mohammadi
Dec 28, 2018·Combinatorial Chemistry & High Throughput Screening·ShaoPeng WangYudong Cai
Aug 17, 2018·Experimental and Therapeutic Medicine·Dena KeLina Jing
Mar 2, 2017·Medical & Biological Engineering & Computing·Nuh Hatipoglu, Gokhan Bilgin
Feb 23, 2021·Journal of Healthcare Engineering·Jingjing YangYanan Liu

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