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- Title
A New Deformable Model Based on Level Sets for Medical Image Segmentation.
- Authors
Jayadevappa, D.; Kumar, S. Srinivas; Murty, D. S.
- Abstract
This paper presents a new deformable model based on level sets for medical image segmentation which plays a pivotal role in medical diagnosis. The current popular Image segmentation deformable models such as Snakes, Geometric Active Contours, Gradient Vector Flow, Level sets and Variational Level sets have a limitation that the convergence of the contour towards the object boundary is slow and hence not suitable for real time medical diagnosis. To counter this limitation we present an improved image segmentation algorithm which is computationally efficient and also the proximity of the contour towards the object is higher compared to existing algorithms. A new speed term is introduced in the evolution step of variational level set in order to speed up the convergence process. The variational level sets in images with intensity inhomogeneity, tend to be slower and prone to leakage of contour outside the object boundary. This is due to the selection of gradient information for the termination of convergence process. However, this limitation is overcome in the proposed algorithm by modifying the edge indicator function embedded with the speed term that optimizes the effective distance of the attractive force. Experimental results are provided using real time medical images. Comparative tables and graphs highlighting the performance of various deformable models are also presented.
- Subjects
DIAGNOSIS; IMAGING systems; COMPARATIVE studies; LEVEL set methods; ALGORITHMS; STOCHASTIC convergence
- Publication
IAENG International Journal of Computer Science, 2009, Vol 36, Issue 3, p199
- ISSN
1819-656X
- Publication type
Article