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Proceedings Paper

Genetic snakes for medical image segmentation
Author(s): Lucia Ballerini
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Paper Abstract

In this paper an approach is described for segmenting medical images. We use active contour model, also known as snakes, and we propose an energy minimization procedure based on Genetic Algorithms (GA). The widely recognized power of deformable models stems from their ability to segment anatomic structures by exploiting constraints derived from the image data together with a priori knowledge about the location, size, and shape of these structures. The application of snakes to extract region of interest is, however, not without limitations. As is well known, there may be a number of problems associated with this approach such as initialization, existence of multiple minima, and the selection of elasticity parameters. We propose the use of GA to overcome these limits. GAs offer a global search procedure that has shown its robustness in many tasks, and they are not limited by restrictive assumptions as derivatives of the goal function. GAs operate on a coding of the parameters (the positions of the snake) and their fitness function is the total snake energy. We employ a modified version of the image energy which consider both the magnitude and the direction of the gradient and the Laplacian of Gaussian. Experimental results on medical images are reported. Images used in this work are ocular fundus images, snakes result very useful in the segmentation of the Foveal Avascular Zone. The experiments performed with ocular fundus images show that the proposed method is promising in the early detection of the diabetic retinopathy.

Paper Details

Date Published: 24 September 1998
PDF: 12 pages
Proc. SPIE 3457, Mathematical Modeling and Estimation Techniques in Computer Vision, (24 September 1998); doi: 10.1117/12.323453
Show Author Affiliations
Lucia Ballerini, Univ. of Florence (Sweden)

Published in SPIE Proceedings Vol. 3457:
Mathematical Modeling and Estimation Techniques in Computer Vision
Francoise J. Preteux; Jennifer L. Davidson; Edward R. Dougherty, Editor(s)

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