Lymphocyte segmentation using the transferable belief model

Authors:
Costas Panagiotakis;Emmanuel Ramasso;Georgios Tziritas
Affiliations:
Department of Computer Science, University of Crete, Heraklion, Greece;FEMTO-ST Institute, UMR CNRS, UFC, ENSMM, UTBM, Automatic Control and Micro-Mechatronic Systems Department, France;Department of Computer Science, University of Crete, Heraklion, Greece
Venue:
ICPR'10 Proceedings of the 20th International conference on Recognizing patterns in signals, speech, images, and videos
Year:
2010

Citing 3
Cited 2

The transferable belief model

Artificial Intelligence
Any dimension polygonal approximation based on equal errors principle

Pattern Recognition Letters
Decision making in the TBM: the necessity of the pignistic transformation

International Journal of Approximate Reasoning

Pattern recognition in histopathological images: an ICPR 2010 contest

ICPR'10 Proceedings of the 20th International conference on Recognizing patterns in signals, speech, images, and videos
Learning to detect cells using non-overlapping extremal regions

MICCAI'12 Proceedings of the 15th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part I

Quantified Score

Hi-index	0.00

Visualization

Abstract

In the context of several pathologies, the presence of lymphocytes has been correlated with disease outcome. The ability to automatically detect lymphocyte nuclei on histopathology imagery could potentially result in the development of an image based prognostic tool. In this paper we present a method based on the estimation of a mixture of Gaussians for determining the probability distribution of the principal image component. Then, a post-processing stage eliminates regions, whose shape is not similar to the nuclei searched. Finally, a Transferable Belief Model is used to detect the lymphocyte nuclei, and a shape based algorithm possibly splits them under an equal area and an eccentricity constraint principle.