Nonparametric MRI segmentation using mean shift and edge confidence maps

Juan Ramon Jimenez; Veronica Medina; Oscar Yanez

doi:10.1117/12.480121

15 May 2003 Nonparametric MRI segmentation using mean shift and edge confidence maps

Juan Ramon Jimenez, Veronica Medina, Oscar Yanez

Proceedings Volume 5032, Medical Imaging 2003: Image Processing; (2003) https://doi.org/10.1117/12.480121
Event: Medical Imaging 2003, 2003, San Diego, California, United States

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.

Citation Download Citation

Juan Ramon Jimenez, Veronica Medina, and Oscar Yanez "Nonparametric MRI segmentation using mean shift and edge confidence maps", Proc. SPIE 5032, Medical Imaging 2003: Image Processing, (15 May 2003); https://doi.org/10.1117/12.480121

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