AUTOMATED CELL COUNTING AND CLUSTER SEGMENTATION USING CONCAVITY DETECTION AND ELLIPSE FITTING TECHNIQUES

• Type of Material: Conference Paper
• Authors: Sonal Kothari, Georgia Institute of Technology; Qaiser Chaudry, Georgia Institute of Technology; Dongmei Wang, Emory University
• Language: English
• Date: 2009-08-07
• Publisher: Emory University Libraries
• Publication Version: Author Accepted Manuscript (After Peer Review)
• Copyright Statement: © 2009 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, including reprinting/republishing this material for advertising or promotional purposes, collecting new collected works for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
• Final Published Version (URL): http://dx.doi.org/10.1109/ISBI.2009.5193169
• Title of Journal or Parent Work: 2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, Vols. 1 and 2
• Conference or Event Name: IEEE Internaional Symposium on Biomedical Imaging - From Nano to Macro
• Start Page: 795
• End Page: 798
• Grant/Funding Information: This research has been supported by grants from National Institutes of Health (Bioengineering Research Partnership R01CA108468, P20GM072069, CCNE U54CA119338), Georgia Cancer Coalition, Hewlett Packard, and Microsoft Research.
• Keywords: Technology; concavity detection; digital tissue samples; Radiology, Nuclear Medicine & Medical Imaging; cell counting; cluster segmentation; Engineering; ellipse fitting; Life Sciences & Biomedicine; Science & Technology; Engineering, Biomedical
• Research Categories: Engineering, Biomedical

Abstract

This paper presents a novel, fast and semi-automatic method for accurate cell cluster segmentation and cell counting of digital tissue image samples. In pathological conditions, complex cell clusters are a prominent feature in tissue samples. Segmentation of these clusters is a major challenge for development of an accurate cell counting methodology. We address the issue of cluster segmentation by following a three step process. The first step involves pre-processing required to obtain the appropriate nuclei cluster boundary image from the RGB tissue samples. The second step involves concavity detection at the edge of a cluster to find the points of overlap between two nuclei. The third step involves segmentation at these concavities by using an ellipse-fitting technique. Once the clusters are segmented, individual nuclei are counted to give the cell count. The method was tested on four different types of cancerous tissue samples and shows promising results with a low percentage error, high true positive rate and low false discovery rate.