Spatial Shape Constrained Fuzzy C-Means (FCM) Clustering for Nucleus Segmentation in Pap smear Images

Ratna Saha, Mariusz Bajger, Gobert Lee

Research output: Contribution to conferencePaperpeer-review

24 Citations (Scopus)

Abstract

Precise segmentation of Pap smear cell nucleus is crucial for early diagnosis of cervical cancer. This
task is particularly challenging because of cell overlapping, inconsistent staining, poor contrast and
other imaging artifacts. In this study, a novel method is proposed to segment cell nucleus from
overlapping Pap smear cell images. The proposed technique introduces a circular shape function
(CSF) to increase the robustness of Pap cell nucleus segmentation using fuzzy c-means clustering.
CSF imposes a shape constrain over the formed clusters, while improves the boundary of the
nucleus. The shape function helps to differentiate the pixels having similar intensity value but located
in different spatial regions. The method is evaluated using Overlapping Cervical Cytology
Image Segmentation Challenge - ISBI 2014 dataset and compared with the traditional FCM clustering
and recently published state-of-the-art methods. Both qualitative and quantitative measures indicate
that the new technique performs favorably with others.
Original languageEnglish
Pages1-8
Number of pages8
DOIs
Publication statusPublished - 2016
Event2016 International Conference on Digital Image Computing: Techniques and Applications (DICTA) - Gold Coast, Australia, Gold Coast, Australia
Duration: 30 Nov 20162 Dec 2016

Conference

Conference2016 International Conference on Digital Image Computing: Techniques and Applications (DICTA)
Abbreviated titleDICTA
Country/TerritoryAustralia
CityGold Coast
Period30/11/162/12/16

Fingerprint

Dive into the research topics of 'Spatial Shape Constrained Fuzzy C-Means (FCM) Clustering for Nucleus Segmentation in Pap smear Images'. Together they form a unique fingerprint.

Cite this