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Proceedings Paper

Automatic segmentation of MR brain images
Author(s): Nigel John; Xiaohong Li; Akmal Younis; Mansur R. Kabuka
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Paper Abstract

An automatic image segmentation for MR brain images based on the gray level characteristics of the images is developed. The method analyses a sequence of MR brain images to provide region information as well as boundary data for classification and eventual creation of 3D models. The system incorporates global information from the image set through an analysis of the statistics of the cooccurrence matrices. Local consistency is then applied with the use of a relaxation algorithm on individual images. The cooccurrence matrices provide conditional probabilities for the classification of pixels into specific regions or boundaries based on the matrix distribution. A constrained stochastic relaxation is then used to refine the probabilistic labels using local image information. Results of the technique are presented for MR brain images.

Paper Details

Date Published: 11 May 1994
PDF: 12 pages
Proc. SPIE 2167, Medical Imaging 1994: Image Processing, (11 May 1994); doi: 10.1117/12.175107
Show Author Affiliations
Nigel John, Univ. of Miami (United States)
Xiaohong Li, Univ. of Miami (United States)
Akmal Younis, Univ. of Miami (United States)
Mansur R. Kabuka, Univ. of Miami (United States)


Published in SPIE Proceedings Vol. 2167:
Medical Imaging 1994: Image Processing
Murray H. Loew, Editor(s)

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