Fuzzy Clustering and Active Contours for Histopathology Image Segmentation and Nuclei Detection

Authors:
Adel Hafiane;Filiz Bunyak;Kannappan Palaniappan
Affiliations:
Department of Computer Science, University of Missouri-Columbia, Columbia, USA MO 65211;Department of Computer Science, University of Missouri-Columbia, Columbia, USA MO 65211;Department of Computer Science, University of Missouri-Columbia, Columbia, USA MO 65211
Venue:
ACIVS '08 Proceedings of the 10th International Conference on Advanced Concepts for Intelligent Vision Systems
Year:
2008

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Abstract

Histopathology imaging provides high resolution multispectral images for study and diagnosis of various types of cancers. The automatic analysis of these images can greatly facilitate the diagnosis task for pathologists. A primary step in computational histology is accurate image segmentation to detect the number and spatial distribution of cell nuclei in the tissue, along with segmenting other guiding structures such as lumen and epithelial regions which together make up a gland structure. This paper presents a new method for gland structure segmentation and nuclei detection. In the first step, fuzzy c-means with spatial constraint algorithm is applied to detect the potential regions of interest, multiphase vector-based level set algorithm is then used to refine the segmentation. Finally, individual nucleus centers are detected from segmented nuclei clusters using iterative voting algorithm. The obtained results show high performances for nuclei detection compared to the human annotation.