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Proceedings Paper

Unsupervised statistical segmentation of multispectral volumetric MRI images
Author(s): Jose Gerardo Tamez-Pena; Saara Totterman; Kevin J. Parker
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Paper Abstract

This work presents a reliable automatic segmentation algorithm for multispectral MRI data sets. We propose the use of an automatic statistical region growing algorithm based on a robust estimation of local region mean and variance for every voxel on the image. The best region growing parameters are automatically found via the minimization of a cost functional. Furthermore, we propose a hierarchical use of relaxation labeling, region splitting, and constrained region merging to improve the quality of the MRI segmentation. We applied this approach to the segmentation of MRI images of anatomically complex structures which suffer signal fading and noise degradations.

Paper Details

Date Published: 21 May 1999
PDF: 12 pages
Proc. SPIE 3661, Medical Imaging 1999: Image Processing, (21 May 1999); doi: 10.1117/12.348585
Show Author Affiliations
Jose Gerardo Tamez-Pena, Univ. of Rochester (United States)
Saara Totterman, Univ. of Rochester Medical Ctr. (United States)
Kevin J. Parker, Univ. of Rochester (United States)


Published in SPIE Proceedings Vol. 3661:
Medical Imaging 1999: Image Processing
Kenneth M. Hanson, Editor(s)

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