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, locations, and textures. Our approach has important implications for the generation of lesion overlap maps of a given population and the assessment of lesion-deficit mappings. From a clinical perspective, our method should help to compute the total volume of lesion or to trace precisely lesion boundaries that might be pertinent for surgical or diagnostic purposes.
journal_name
Neuroimagejournal_title
NeuroImageauthors
Seghier ML,Ramlackhansingh A,Crinion J,Leff AP,Price CJdoi
10.1016/j.neuroimage.2008.03.028subject
Has Abstractpub_date
2008-07-15 00:00:00pages
1253-66issue
4eissn
1053-8119issn
1095-9572pii
S1053-8119(08)00255-3journal_volume
41pub_type
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