Automatic segmentation of breast tumors in mammography using fuzzy clustering
Sarbjit Kaur, Jasmeen Gill · AIP conference proceedings · 2022
Image segmentation is the process via which the digital image understudy is partitioned into multiple segments. The purpose of the segmentation is to convert the representation of the image into something more significant and easier to examine. The primary aim of image segmentation in mammography is to find the hazardous objects (lesions, dense breast tissues, and micro calcifications) and boundaries of the mammograms. Segmentation allocates a tag to every individual pixel in an image in such a routine that the pixels with the similar tag share certaintopographies. The research paper elaborates a proposed method for performing segmentation of the mammograms. The MIAS dataset have been used to implement the research on 16 specific mammogram images. The proposed model is detailed via a flowchart followed by an elaborated explanation specifying the adopted process for conducting the segmentation. The techniques used are GCC “Green Channel Complement”, CLAHE “Contrast Limited Adaptive Histogram Equalization”, Morphological operations, and FCM “Fuzzy C-Means”. The values of 15 performance evaluation parameters for 16 different mammograms have been obtained to prove the worthiness of the conducted research. The GUIis designed in such a manner that it makes implementation easy and contented.