Please use this identifier to cite or link to this item: http://hdl.handle.net/10553/47459
Title: An Efficient Algorithm for Multiple Sclerosis Lesion Segmentation from Brain MRI
Authors: Cardenes, R.
Warfield, S. K.
Macias, EM 
Santana, J. A. 
Ruiz-Alzola, J. 
UNESCO Clasification: 3314 Tecnología médica
Keywords: Finding Nearest Neighbors
Arbitrary Dimensions
Classification
Issue Date: 2003
Publisher: 0302-9743
Journal: Lecture Notes in Computer Science 
Conference: 9th International Workshop on Computer Aided Systems Theory 
Abstract: We propose a novel method for the segmentation of Multiple Sclerosis (MS) lesions in MRI. The method is based on a three-step approach: first a conventional k-NN classifier is applied to pre-classify gray matter (CM), white matter (WM), cerebro-spinal fluid (CSF) and MS lesions from a set of prototypes selected by an expert. Second, the classification of problematic patterns is resolved computing a fast distance transformation (DT) algorithm from the set of prototypes in the Euclidean space defined by the MRI dataset. Finally, a connected component filtering algorithm is used to remove lesion voxels not connected to the real lesions. This method uses distance information together with intensity information to improve the accuracy of lesion segmentation and, thus, it is specially useful when MS lesions have similar intensity values than other tissues. It is also well suited for interactive segmentations due to its efficiency. Results are shown on real MRI data as wall as on a standard database of synthetic images.
URI: http://hdl.handle.net/10553/47459
ISBN: 3-540-20221-8
ISSN: 0302-9743
Source: LECTURE NOTES IN COMPUTER SCIENCE[ISSN 0302-9743], p. 542-551
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