Segmentation is an important step for the diagnosis of multiple sclerosis. In this paper, a method for segmentation of multiple sclerosis lesions from Magnetic Resonance (MR) brain image is proposed. The proposed method combines the strengths of two existing techniques: fuzzy connectedness and artificial neural networks. From the input MR brain image, the fuzzy connectedness algorithm is used to extract segments which are parts of Cerebrospinal Fluid (CSF), White Matter (WM) or Gray Matter (GM). Segments of the MRI image which are not extracted as part of CSF, WM or GM are processed morphologically, and features are computed for each of them. Then these computed features are fed to a trained artificial neural network, which decides whether a segment is a part of a lesion or not. The results of our method show 90% correlation with the expert's manual work.

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Abstract. A deformable shape model called Active Shape Structural Model (ASSM) is used within a biometric framework to define a biometric sketch recognition algorithm. Experimental results show that mainly structural relations rather than statistical features can be used to recognize sketches of different users with high accuracy.

An image analysis system to segment multiple sclerosis lesions of (MR) brain volumes is proposed. The method uses Markov Random Fields (MRF) both at low and high levels. The neighborhood system used in this MRF is defined in three types: (1) Voxel to voxel: a low-level heterogeneous neighborhood used to restore noisy images. (2) Voxel to segment: a fuzzy atlas is registered elastically with the MRF then used as a-priori knowledge to correct miss-classified voxels. (3) Segment to segment: Lesion candidates are processed by a feature based classifier that looks at unary and neighborhood information to eliminate false positives.

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