Shape related signatures of nuclei in a tissue section are important for diagnosis and prognosis of cancer. Understandably, the process of demarcation of nuclei for cytometry with high degree of confidence is the most difficult part as the tissue section is fraught with staining artifacts and frequently contains other objects such as overlapping nuclei, nuclear debris, and extracellular structures. In this work, we address this problem using a novel clustering algorithm for various shapes in prostate histopathology images using an unsupervised manifold learning paradigm. Experimental results with two-dimensional embedding of the shapes using diffusion maps demonstrate that various shapes in the tissue section are organized in accordance to the degree of complexity of their boundaries. This important observation can be exploited in the development of computerized techniques for image based cytometry.

 

 

This work is in collaboration with Dr Muhammad Arif (from PIEAS), who until recently was a visiting Postodoctoral fellow at our group.

Relevant Publications

• M Arif, NM Rajpoot,
  Classification of Potential Nuclei in Prostate Histology Images using Shape Manifold Learning,
  in Proceedings International Conference on Machine Vision (ICMV'2007), December 2007.
  
• NM Rajpoot, M Arif,
  Learning the Shape Manifolds using Diffusion Maps,
  presented at the BMVA Symposium on Shape Representation, Analysis, and Perception, November 2007.
  
• NM Rajpoot, M Arif, AH Bhalerao,
  Unsupervised Learning of Shape Manifolds,
  in Proceedings British Machine Vision Conference (BMVC'2007), September 2007.
  
• M Arif, NM Rajpoot,
  Detection of Nuclei by Unsupervised Manifold Learning,
  in Proceedings Medical Image Understanding and Analysis (MIUA'2007), July 2007.