Segmentation of Multi-Channel Image with Markov Random Field Based Active Contour Model
Journal of VLSI Signal Processing Systems
Scalable multiresolution color image segmentation
Signal Processing
An EM/E-MRF algorithm for adaptive model based tracking in extremely poor visibility
Image and Vision Computing
An improved statistical approach for cerebrovascular tree extraction
Miar'06 Proceedings of the Third international conference on Medical Imaging and Augmented Reality
| Hi-index | 0.00 |
A new robust method to segment images based on Markov random fields (MRF) is presented. The algorithm does not require the number of classes or regions K as input, which is normally difficult to determine in advance. There is also no need for an initial estimate obtained by an algorithm such as K-means. Further, each region is connected during the whole segmentation process leading to more reliable estimates of the regions' mean gray levels and to fewer wrong detected boundaries. In addition, a novel way to incorporate edge information into the segmentation process is proposed resulting in a better detection of small objects. Experimental results demonstrate the performance of our technique.