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

• Authors: Sonal Kothari, Georgia Institute of Technology; Qaiser Chaudry, Georgia Institute of Technology; Dongmei Wang, Emory University
• Proceedings Title: 2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, Vols. 1 and 2
• Conference Name: IEEE Internaional Symposium on Biomedical Imaging - From Nano to Macro
• Publisher: IEEE
• Conference Place: Boston, MA
• Publication Date: 2009-08-07
• Type of Work: Conference | Post-print: After Peer Review
• Publisher DOI: http://doi.org/10.1109/ISBI.2009.5193169
• Subjects: Engineering, Biomedical
• Keywords: Science & Technology; Technology; Life Sciences & Biomedicine; Engineering, Biomedical; Radiology, Nuclear Medicine & Medical Imaging; Engineering; cluster segmentation; cell counting; ellipse fitting; concavity detection; digital tissue samples

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.