Fuzzy Local Information C-means Algorithm for Histopathological Image Segmentation

被引：1

作者：

Cetin, Mustafa ^{[1
]}

Dokur, Zumray ^{[1
]}

Olmez, Tamer ^{[1
]}

机构：

[1] Istanbul Tech Univ, Dept Elect & Commun Engn, Istanbul, Turkey

来源：

2019 SCIENTIFIC MEETING ON ELECTRICAL-ELECTRONICS & BIOMEDICAL ENGINEERING AND COMPUTER SCIENCE (EBBT) | 2019年

关键词：

fuzzy c-means; fuzzy local information c-means; clustering; histopathological image segmentation; nuclei segmentation; computer aided diagnosis; NUCLEI;

D O I：

10.1109/ebbt.2019.8742034

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

Accurate analysis of cellular structures has great importance for cancer diagnosis in histopathological images. Manual analysis of sections carried out by pathologists is time-consuming and costly. Analysis of cell structures with computer aid supports pathologists to diagnose cancer easily. In this paper, automated cell nuclei segmentation from histopathological images is investigated by using Fuzzy Local Information C-means Clustering (FLICM) Method. The Cancer Genome Atlas data set annotated by expert pathologists is used to evaluate the method. Compared with the other related studies, the highest f-measure and overlap values are obtained with this method.

引用

页数：6

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