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Proceedings Paper

Nonparametric MRI segmentation using mean shift and edge confidence maps
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Paper Abstract

In this paper, a nonparametric statistical segmentation procedure based on the computation of the mean shift within the joint space-range feature representation of brain MR images is presented. The mean shift is a simple, nonparametric estimator, which can be implemented in a data-driven approach. The number of classes and other initialization parameters are not needed to compute the mean shift. The procedure estimates the local modes of the probability density function in order to define the cluster centers on the feature space. Local segmentation quality is improved by including a measure of edge confidence among adjacent segmented regions. This measure drives the iterative application of transitive closure operations on the region adjacency graph until convergence to a stable set of regions. In this manner, edge detection and region segmentation techniques are combined for the extraction of weak but significant edges from brain images. With the proposed methodology, the modes of the classes' distribution can be robustly estimated and homogeneous regions defined, but also fine borders are preserved. The main contribution of this work is the combined use of mean shift estimation, together with a robust, edge-oriented region fusion technique to delineate structures in brain MRI.

Paper Details

Date Published: 15 May 2003
PDF: 9 pages
Proc. SPIE 5032, Medical Imaging 2003: Image Processing, (15 May 2003); doi: 10.1117/12.480121
Show Author Affiliations
Juan Ramon Jimenez, Univ. Autonoma Metropolitana-Iztapalapa (Mexico)
Veronica Medina, Univ. Autonoma Metropolitana-Iztapalapa (Mexico)
Oscar Yanez, Univ. Autonoma Metropolitana-Iztapalapa (Mexico)


Published in SPIE Proceedings Vol. 5032:
Medical Imaging 2003: Image Processing
Milan Sonka; J. Michael Fitzpatrick, Editor(s)

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