Cytoplasm and nucleus segmentation in cervical smear images using Radiating GVF Snake

Authors:
Kuan Li;Zhi Lu;Wenyin Liu;Jianping Yin
Affiliations:
College of Computer Science, National University of Defense Technology, Changsha, Hunan 410073, China and Department of Computer Science, City University of Hong Kong, Hong Kong, China;Department of Computer Science, City University of Hong Kong, Hong Kong, China;Department of Computer Science, City University of Hong Kong, Hong Kong, China;College of Computer Science, National University of Defense Technology, Changsha, Hunan 410073, China
Venue:
Pattern Recognition
Year:
2012

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Abstract

A Radiating Gradient Vector Flow (RGVF) Snake aiming at accurate extraction of both the nucleus and cytoplasm from a single-cell cervical smear image is proposed. After preprocessing, the areas in the image are roughly clustered into nucleus, cytoplasm and the background by a spatial K-means clustering algorithm. After initial contours are extracted, the image is segmented using RGVF. RGVF involves a new edge map computation method and a stack-based refinement, and is thus robust to contaminations and can effectively locate the obscure boundaries. The boundaries can also be correctly traced even if there are interferences near the cytoplasm and nucleus regions. Experiments performed on the Herlev dataset, which contains 917 images show the effectiveness of the proposed algorithm.