Automated segmentation of free-lying cell nuclei in Pap smears for malignancy-associated change analysis
Paper in proceeding, 2012
This paper presents an automated algorithm for robustly detecting and segmenting free-lying cell nuclei in bright-field microscope images of Pap smears. This is an essential initial step in the development of an automated screening system for cervical cancer based on malignancy associated change (MAC) analysis. The proposed segmentation algorithm makes use of gray-scale annular closings to identify free-lying nuclei-like objects together with marker-based watershed segmentation to accurately delineate the nuclear boundaries. The algorithm also employs artifact rejection based on size, shape, and granularity to ensure only the nuclei of intermediate squamous epithelial cells are retained. An evaluation of the performance of the algorithm relative to expert manual segmentation of 33 fields-of-view from 11 Pap smear slides is also presented. The results show that the sensitivity and specificity of nucleus detection is 94.71% and 85.30% respectively, and that the accuracy of segmentation, measured using the Dice coefficient, of the detected nuclei is 97.30±1.3%.
Microscope images
Pap smear
Manual segmentation
Automated algorithms
Sensitivity and specificity
Change analysis
Watershed segmentation
Dice coefficient
Automated segmentation
Gray scale
Cervical cancers
Automated screening
Cell nucleus
Segmentation algorithms
Epithelial cells
Brightfield
Author
Ramin Moshavegh
Chalmers, Signals and Systems
B. Ehteshami Bejnordi
Chalmers, Signals and Systems
Andrew Mehnert
Chalmers, Signals and Systems, Signal Processing and Biomedical Engineering
K. Sujathan
Regional Cancer Centre India
P. Malm
Uppsala University
E. Bengtsson
Uppsala University
Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
1557170X (ISSN)
5372-5375 6347208978-142444119-8 (ISBN)
Subject Categories
Medical Engineering
DOI
10.1109/EMBC.2012.6347208
ISBN
978-142444119-8