Lesion identification using unified segmentation-normalisation models and fuzzy clustering.

ABSTRACT: In this paper, we propose a new automated procedure for lesion identification from single images based on the detection of outlier voxels. We demonstrate the utility of this procedure using artificial and real lesions. The scheme rests on two innovations: First, we augment the generative model used for combined segmentation and normalization of images, with an empirical prior for an atypical tissue class, which can be optimised iteratively. Second, we adopt a fuzzy clustering procedure to identify outlier voxels in normalised gray and white matter segments. These two advances suppress misclassification of voxels and restrict lesion identification to gray/white matter lesions respectively. Our analyses show a high sensitivity for detecting and delineating brain lesions with different sizes,

SUBMITTER: Seghier ML 

PROVIDER: S-EPMC2724121 | biostudies-literature | 2008 Jul

REPOSITORIES: biostudies-literature