Unsupervised statistical segmentation of multispectral volumetric MRI images

Jose Gerardo Tamez-Pena; Saara Totterman; Kevin J. Parker

doi:10.1117/12.348585

21 May 1999 Unsupervised statistical segmentation of multispectral volumetric MRI images

Jose Gerardo Tamez-Pena, Saara Totterman, Kevin J. Parker

Proceedings Volume 3661, Medical Imaging 1999: Image Processing; (1999) https://doi.org/10.1117/12.348585
Event: Medical Imaging '99, 1999, San Diego, CA, United States

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.

Citation Download Citation

Jose Gerardo Tamez-Pena, Saara Totterman, and Kevin J. Parker "Unsupervised statistical segmentation of multispectral volumetric MRI images", Proc. SPIE 3661, Medical Imaging 1999: Image Processing, (21 May 1999); https://doi.org/10.1117/12.348585

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