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Proceedings Paper

Progress in the robust automated segmentation of real cell images
Author(s): P. Bamford; P. Jackway; Brian Lovell
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Paper Abstract

We propose a collection of robust algorithms for the segmentation of cell images from Papanicolaou stained cervical smears (`Pap' smears). This problem is deceptively difficult and often results on laboratory datasets do not carry over to real world data. Our approach is in 3 parts. First, we segment the cytoplasm from the background using a novel method based on the Wilson and Spann multi-resolution framework. Second, we segment the nucleus from the cytoplasm using an active contour method, where the best contour is found by a global minimization method. Third, we implement a method to determine a confidence measure for the segmentation of each object. This uses a stability criterion over the regularization parameter (lambda) in the active contour. We present the results of thorough testing of the algorithms on large numbers of cell images. A database of 20,120 images is used for the segmentation tests and 18,718 images for the robustness tests.

Paper Details

Date Published: 8 July 1999
PDF: 23 pages
Proc. SPIE 3747, New Approaches in Medical Image Analysis, (8 July 1999); doi: 10.1117/12.351626
Show Author Affiliations
P. Bamford, Univ. of Queensland (Australia)
P. Jackway, Univ. of Queensland (Australia)
Brian Lovell, Univ. of Queensland (Australia)


Published in SPIE Proceedings Vol. 3747:
New Approaches in Medical Image Analysis
Binh Pham; Michael Braun; Anthony John Maeder; Michael P. Eckert, Editor(s)

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