#134: Histopathology tissue segmentation by combining fuzzy clustering with multiphase vector level sets

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

Software Tools and Algorithms for Biological Systems, Springer, pgs. 413--424, 2011

histopathology, active contours, classification, features, texture, machine learning, data mining, biomedical

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


High resolution, multispectral, and multimodal imagery of tissue biopsies is an indispensable source of information for diagnosis and prognosis of diseases. Automatic extraction of relevant features from these imagery is a valuable assistance for medical experts. 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 structures such as lumen and epithelial regions which together make up a gland structure. This chapter presents an automatic segmentation system for histopathology imaging. Spatial constraint fuzzy C-means provides an unsupervised initialization. An active contour algorithm that combines multispectral edge and region informations through a vector multiphase level set framework and Beltrami color metric tensors refines the segmentation. An improved iterative kernel filtering approach detects individual nuclei centers and decomposes densely clustered nuclei structures. The obtained results show high performances for nuclei detection compared to the human annotation.