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A method that considerably reduces the computational and memory complexities associated with the generation of high-dimensional (&gt; or =3) feature maps for image segmentation is described. The method is based on the K-nearest neighbor (KNN) classification and consists of two parts: preprocessing of feature space and fast KNN. This technique is implemented on a PC and applied for generating 3D and 4D feature maps for segmenting MR brain images of multiple sclerosis patients.

Implementation of High-Dimensional Feature Map for Segmentation of MR Images