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

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.

[1]  Guna Seetharaman,et al.  Flux Tensor Constrained Geodesic Active Contours with Sensor Fusion for Persistent Object Tracking , 2007, J. Multim..

[2]  Guillermo Sapiro,et al.  Geodesic Active Contours , 1995, International Journal of Computer Vision.

[3]  Oliver Schmitt,et al.  Radial symmetries based decomposition of cell clusters in binary and gray level images , 2008, Pattern Recognit..

[4]  Tony F. Chan,et al.  A Multiphase Level Set Framework for Image Segmentation Using the Mumford and Shah Model , 2002, International Journal of Computer Vision.

[5]  A. Madabhushi,et al.  Gland Segmentation and Computerized Gleason Grading of Prostate Histology by Integrating Low-, High-level and Domain Specific Information , 2007 .

[6]  D. Mumford,et al.  Optimal approximations by piecewise smooth functions and associated variational problems , 1989 .

[7]  Christophe Zimmer,et al.  Segmenting and tracking fluorescent cells in dynamic 3-D microscopy with coupled active surfaces , 2005, IEEE Transactions on Image Processing.

[8]  K. Plataniotis,et al.  Color Image Processing : Methods and Applications , 2006 .

[9]  Tony F. Chan,et al.  Active Contours without Edges for Vector-Valued Images , 2000, J. Vis. Commun. Image Represent..

[10]  Anant Madabhushi,et al.  AUTOMATED GRADING OF PROSTATE CANCER USING ARCHITECTURAL AND TEXTURAL IMAGE FEATURES , 2007, 2007 4th IEEE International Symposium on Biomedical Imaging: From Nano to Macro.

[11]  Bertrand Zavidovique,et al.  A modified FCM with optimal Peano scans for image segmentation , 2005, IEEE International Conference on Image Processing 2005.

[12]  Xiaobo Zhou,et al.  Automatic Segmentation of High-Throughput RNAi Fluorescent Cellular Images , 2008, IEEE Transactions on Information Technology in Biomedicine.

[13]  Qing Yang,et al.  Iterative Voting for Inference of Structural Saliency and Characterization of Subcellular Events , 2007, IEEE Transactions on Image Processing.

[14]  Tony F. Chan,et al.  Active contours without edges , 2001, IEEE Trans. Image Process..