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Proceedings Paper

Adaptive RBF network with active contour coupling for multispectral MRI segmentation
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Paper Abstract

A segmentation procedure using a radial basis function network (RBFN), coupled with an active contour (AC) model based on a cubic splines formulation is presented for the detection of the gray-white matter boundary in axial MMRI (T1, T2 and PD). A RBFN classifier has been previously introduced for MMRI segmentation, with good generalization at a rate of 10% misclassification over white and gray matter pixels on the validation set. The coupled RBFN and AC model system incorporates the posterior probability estimation map into the AC energy term as a restriction force. The RBFN output is also employed to provide an initial contour for the AC. Furthermore, an adaptation strategy for the network weights, guided by a feedback from the contour model adjustment at each iteration, is described. In order to compare the algorithm's performance, the segmentations using the adaptive, as well as the non-adaptive schemes were computed. It was observed that the major differences are located around deep circonvolutions, where the result of the adaptive process is superior than that obtained with the non-adaptive scheme, even in moderate noise conditions. In summary, the RBFN provides a good initial contour for the AC, the coupling of both processes keeps the final contour within the desired region and the adaptive strategy enhances the contour location.

Paper Details

Date Published: 9 May 2002
PDF: 12 pages
Proc. SPIE 4684, Medical Imaging 2002: Image Processing, (9 May 2002); doi: 10.1117/12.467130
Show Author Affiliations
Raquel Valdes-Cristerna, Univ. Autonoma Metropolitana/Iztapalapa (Mexico)
Veronica Medina, Univ. Autonoma Metropolitana/Iztapalapa (Mexico)
Oscar Yanez-Suarez, Univ. Autonoma Metropolitana/Iztapalapa (Mexico)

Published in SPIE Proceedings Vol. 4684:
Medical Imaging 2002: Image Processing
Milan Sonka; J. Michael Fitzpatrick, Editor(s)

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