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8 March 2007 Improved CSF classification and lesion detection in MR brain images with multiple sclerosis
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The study deals with the challenging task of automatic segmentation of MR brain images with multiple sclerosis lesions (MSL). Multi-Channel data is used, including "fast fluid attenuated inversion recovery" (fast FLAIR or FF), and statistical modeling tools are developed, in order to improve cerebrospinal fluid (CSF) classification and to detect MSL. Two new concepts are proposed for use within an EM framework. The first concept is the integration of prior knowledge as it relates to tissue behavior in different MRI modalities, with special attention given to the FF modality. The second concept deals with running the algorithm on a subset of the input that is most likely to be noise- and artifact-free data. This enables a more reliable learning of the Gaussian mixture model (GMM) parameters for brain tissue statistics. The proposed method focuses on the problematic CSF intensity distribution, which is a key to improved overall segmentation and lesion detection. A level-set based active contour stage is performed for lesion delineation, using gradient and shape properties combined with previously learned region intensity statistics. In the proposed scheme there is no need for preregistration of an atlas, a common characteristic in brain segmentation schemes. Experimental results on real data are presented.
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Yulian Wolff, Shmuel Miron M.D., Anat Achiron M.D., and Hayit Greenspan "Improved CSF classification and lesion detection in MR brain images with multiple sclerosis", Proc. SPIE 6512, Medical Imaging 2007: Image Processing, 65122P (8 March 2007);

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