Circular shape constrained fuzzy clustering (CiscFC) for nucleus segmentation in Pap smear images

Ratna Saha, Mariusz Bajger, Gobert Lee

Research output: Contribution to journalArticlepeer-review

14 Citations (Scopus)

Abstract

Accurate detection and segmentation of cell nucleus is the precursor step towards computer
aided analysis of Pap smear images. This is a challenging and complex task due to degree of
overlap, inconsistent staining and poor contrast. In this paper, a novel nucleus segmentation method
is proposed by incorporating a circular shape function in fuzzy clustering. The proposed method was evaluated quantitatively and qualitatively using the Overlapping Cervical Cytology Image Segmentation Challenge - ISBI 2014 challenge dataset comprised of 945 overlapping Pap smear images. It achieved superior performance in terms of Dice similarity coefficient of 0.938, pixel-based recall 0.939 and object based precision 0.968. The results were compared with the standard fuzzy c-means (FCM) clustering, ISBI 2014 challenge submissions and recent state-of-the-art methods. The outcome shows that the new approach can produce more accurate nucleus boundaries while keeping high level of precision and
recall.
Original languageEnglish
Pages (from-to)13-23
Number of pages11
JournalComputers in Biology and Medicine
Volume85
DOIs
Publication statusPublished - 2017

Fingerprint Dive into the research topics of 'Circular shape constrained fuzzy clustering (CiscFC) for nucleus segmentation in Pap smear images'. Together they form a unique fingerprint.

Cite this