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Proceedings Paper

Atlas-guided segmentation of brain images via optimizing neural networks
Author(s): Gene R. Gindi; Anand Rangarajan; I. G. Zubal
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Paper Abstract

Automated segmentation of magnetic resonance (MR) brain imagery into anatomical regions is a complex task that appears to need contextual guidance in order to overcome problems associated with noise, missing data, and the overlap of features associated with different anatomical regions. In this work, the contextual information is provided in the form of an anatomical brain atlas. The atlas provides defaults that supplement the low-level MR image data and guide its segmentation. The matching of atlas to image data is represented by a set of deformable contours that seek compromise fits between expected model information and image data. The dynamics that deform the contours solves both a correspondence problem (which element of the deformable contour corresponds to which elements of the atlas and image data?) and a fitting problem (what is the optimal contour that corresponds to a compromise of atlas and image data while maintaining smoothness?). Some initial results on simple 2D contours are shown.

Paper Details

Date Published: 29 July 1993
PDF: 11 pages
Proc. SPIE 1905, Biomedical Image Processing and Biomedical Visualization, (29 July 1993); doi: 10.1117/12.148668
Show Author Affiliations
Gene R. Gindi, SUNY/Stony Brook (United States)
Anand Rangarajan, Yale Univ. School of Medicine and Yale Univ. (United States)
I. G. Zubal, Yale Univ. School of Medicine (United States)

Published in SPIE Proceedings Vol. 1905:
Biomedical Image Processing and Biomedical Visualization
Raj S. Acharya; Dmitry B. Goldgof, Editor(s)

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