Anatomical Guided Segmentation with Non-Stationary Tissue Class Distributions in an Expectation-Maximization Framework

K. M. Pohl, S. Bouix, R. Kikinis, W. Eric L. Grimson
IEEE International Symposium on Biomedical Imaging
Pages 81-84
April, 2004

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Abstract

High quality segmentation of brain MR images is a challenging task. To deal with this problem many automatic segmentation methods rely on atlas information of anatomical structures. We will further investigate this line of research by introducing hierarchical representations of anatomical structures in an Expectation-Maximization like framework. This new approach enables us to divide a complex segmentation scenario into less difficult sub-problems reducing the scenario's statistical complexity. We will demonstrate the method's strength by segmenting a set of brain MR images into 31 different anatomical structures as well as comparing it to other methods.


Reference

Pohl KM, Bouix S, Kikinis R, Grimson WEL. Anatomical guided segmentation with non-stationary tissue class distributions in an expectation-maximization framework. In IEEE International Symposium on Biomedical Imaging. Arlington, VA, 2004;81-84.

Grants

NIH P41 RR13218, NIH/NIMH 2RO1 MH40799, NIH RO1 MH50747, NIH/NIMH 2K02 MH01110, VA Merit Awards

Research area

segmentation
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