| Paper title: |
Evolutionary Approach Based on Active Edges Detection for Images Segmentation |
| Published in: | Issue 1, (Vol. 9) / 2015 |
| Publishing date: | 2015-03-31 |
| Pages: | 43-49 |
| Author(s): | SIHEM Slatnia , OKBA Kazar , KHAOULA Arab |
| Abstract. | There are many methods for segmentation which vary strongly in their approach to the problem of image segmentation. In this paper, We specified the study in a particular segmentation method of radiological images based on the active edges detection. The optimize solutions was chosen as the genetic algorithm optimization method, and to compare this formalism with other existing methods, we chose a greedy algorithm is criterion for its timeliness. we propose a method of genetic active edge detection in images gray level. In fact, for the convergence of the edge to the object edges, we use the classic and the greedy method. Indeed, the proposed method is based on the active edges optimization using the genetic algorithms process to minimize a sum various energies, in order to evolve a population of snakes to an individual who has the minimum energy. |
| Keywords: | Active Edge, Genetic Algorithm, Greedy Algorithm, Segmentation, Medical Image |
| References: | 1. Jean-Jacques Rousselle, The active edge, method segmentation, application to medical image, thesis Ph.D july 2003. 2. L.D. Cohen. On active contour models and balloons. Computer Vision, Graphics and Image Processing: Image Understanding, 53(2):211–218, Mars 1991. 3. Williams, D.J. and Shah, M. A fast Algorithm for active contours and curvature estimation, CVIGP computer vision graphics image process : image Understanding, vol.55, n°1, janvier 1992, p.14-26. 4. B. Bhanu, S.Lee, and J. Ming, Adaptive image segmentation using a genetic algorithm. IEEE Trans. Systems Man Cybernet. 1995. 5. Ballerini, L. Genetic Snakes for Medical Images Segmentation. Lectures Notes in Computer Science, vol.1596, ISSN 0301-9743, 1999. 6. J.F. Canny. A computational approach to edge detection. IEEE Transactions on pattern analysis and machine intelligence, pages 679-698, 1986 |
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