Journal:Informatica
Volume 17, Issue 2 (2006), pp. 225–236
Abstract
The two major Markov Random Fields (MRF) based algorithms for image segmentation are the Simulated Annealing (SA) and Iterated Conditional Modes (ICM). In practice, compared to the SA, the ICM provides reasonable segmentation and shows robust behavior in most of the cases. However, the ICM strongly depends on the initialization phase.
In this paper, we combine Bak–Sneppen model and Markov Random Fields to define a new image segmentation approach. We introduce a multiresolution technique in order to speed up the segmentation process and to improve the restoration process. Image pixels are viewed as lattice species of Bak–Sneppen model. The a-posteriori probability corresponds to a local fitness. At each cycle, some objectionable species are chosen for a random change in their fitness values. Furthermore, the change in the fitness of each species engenders fitness changes for its neighboring species. After a certain number of iteration, the system converges to a Maximum A Posteriori estimate. In this multireolution approach, we use a wavelet transform to reduce the size of the system.
Journal:Informatica
Volume 15, Issue 2 (2004), pp. 283–290
Abstract
The paper describes a new method to segment ischemic stroke region on computed tomography (CT) images by utilizing joint features from mean, standard deviation, histogram, and gray level co‐occurrence matrix methods. Presented unsupervised segmentation technique shows ability to segment ischemic stroke region.