Robust and efficient image segmentation approaches using Markov random field models

Modified implementations of simulated annealing (SA) for image segmentation are proposed and evaluated. The segmentation procedure is based on a Markov random field (MRF) model for describing regions within an image. SA offers an iterative approach for computing a set of labels with maximum (MAP) probability. However, this approach is computationally expensive and lacks robustness in noisy environments. We propose a random cost function (RCF) for computing energy function in SA. The proposed modified SA (SA-RCF) method depicts more robust performance for image segmentation than standard SA at the same computational cost. Alternatively, we proposed a multi-resolution (MR) approach based on MRF, which offers robust segmentation for noisy images with significant reduction in the computational cost. Computational cost and segmentation accuracy of each algorithm were examined using a set of simulated head computerized tomography (CT) phantoms. ©