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

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.

► A coarse-to-fine segmentation framework is proposed for single-cell cervical cell images.
► Radiating GVF Snake is proposed based on GVF Snake.
► A new edge map computation method and a stack-based refinement are introduced into Radiating GVF Snake.
► Radiating GVF Snake is robust to contaminations and can effectively locate the relatively obscure boundaries.