#78: Fuzzy clustering and active contours for histopathology image segmentation and nuclei detection


A. Hafiane, F. Bunyak, and K. Palaniappan

Lecture Notes in Computer Science (ACIVS), Volume 5259, pgs. 903--914, 2008

histopathology, active contours, classification, segmentation, features, texture, biomedical

PlainText, Bibtex, PDF, URL, DOI, Google Scholar

Abstract

Histopathology imaging provides high resolution multispec- tral 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 accu- rate image segmentation to detect the number and spatial distribution of cell nuclei in the tissue, along with segmenting other guiding struc- tures 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 ob- tained results show high performances for nuclei detection compared to the human annotation.