Automatic Cell Nucleus Segmentation Using Superpixels and Clustering Methods in Histopathological Images

Authors : Gamze MENDİ, Cafer BUDAK

Pages : 304-309

Doi:10.17694/bajece.864266

View : 53 | Download : 11

Publication Date : 2021-07-30

Article Type : Research Paper

Abstract :It is seen that there is an increase in cancer and cancer-related deaths day by day. Early diagnosis is vital for the early treatment of the cancerous area. Computer-aided programs allow for early diagnosis of unhealthy cells that specialist pathologists diagnose as a result of efforts. In this study, kMeans and Fuzzy C Means methods, which are among the global segmentation methods, and SLIC, Quickshift, Felzenszwalb, Watershed and ERS algorithms, which are among the superpixel segmentation methods, were used for automatic cell nucleus detection in high resolution histopathological images with computer aided programs. As a result of the study, the success performances of the segmentation algorithms were analyzed and evaluated. It is seen that better success is obtained in watershed and FCM algorithms in high resolution histopathological images used. Quickshift and SLIC methods gave better results in terms of precision. It is seen that there are k-Means and FCM algorithms that provide the best performance in F measure insert ignore into journalissuearticles values(F-M); and the true negative rate insert ignore into journalissuearticles values(TNR); is more successful in Quickshift, k-Means and SLIC methods.
Keywords : Segmentation, machine learning, histopathological image analysis, superpixels

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