Histopathology Tissue Segmentation by Combining Fuzzy Clustering with Multiphase Vector Level Sets

  • Filiz Bunyak
  • Adel Hafiane
  • Kannappan Palaniappan
Part of the Advances in Experimental Medicine and Biology book series (AEMB, volume 696)


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.


Active Contour Spatial Constraint Gleason Grade Histopathology Image Geodesic Active Contour 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer Science+Business Media, LLC 2011

Authors and Affiliations

  • Filiz Bunyak
    • 1
  • Adel Hafiane
  • Kannappan Palaniappan
  1. 1.Department of Computer ScienceUniversity of Missouri-ColumbiaColumbiaUSA

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