Unsupervised statistical segmentation of multispectral volumetric MRI images
José G. Tamez‐Peña, Saara M. S. Totterman, Kevin J. Parker · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 1999
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.