Efficient multiresolution approach for image segmentation based on Markov random fields
Nariman Majdi Nasab, Mostafa Analoui · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2001
This paper proposes a computationally efficient hierarchical technique for object detection and segmentation and compare it with two other segmentation algorithms. The segmentation (MR) algorithm is performed at coarse resolution based on a maximum a posteriori (MAP) estimation of the field of pixel classifications, which is modeled a Markov random field (MRF). MR performs segmentation of a given image at coarse resolutions. Each resolution will correspond to a hierarchical level in a quad tree. So the classification of a pixel at one resolution will correspond to the classification of four pixels at the next finer resolution. Using this relationship we segment the image at the coarse resolution, each pixel in coarse resolution can be related to 16 pixels in the finer resolution. To find minimum global energy at coarse resolution, one pixel from 16 of observed image field given the unobserved filed. The MAP estimates the pixel classes given the observed filed. Segmentation process at each individual pixel will be performed by searching randomly in each relative pixel at 4x4 block-pixel to find minimum global energy at coarse resolution. Images from simulated head phantoms, degraded by Gaussian noise, are used for comparison of the proposed method with simulated annealing (SA) and minimum gray level distance (MGLD) approaches. Computational cost and segmentation accuracy of these methods are studied. It is shown that the proposed MR method offers a robust and computationally inexpensive method for segmentation of noisy images.