Combination of Hidden Markov Random Field and Conjugate Gradient for Brain Image Segmentation
This work addresses segmentation for medical imaging applications like tumor detection, but it is incremental as it applies an existing optimization method to a known model.
The paper tackled brain image segmentation by combining Hidden Markov Random Field modeling with the Conjugate Gradient algorithm, using finite differences for derivative approximation, and showed that the proposed approach performs favorably compared to other variants in terms of Dice Coefficient on publicly available images.
Image segmentation is the process of partitioning the image into significant regions easier to analyze. Nowadays, segmentation has become a necessity in many practical medical imaging methods as locating tumors and diseases. Hidden Markov Random Field model is one of several techniques used in image segmentation. It provides an elegant way to model the segmentation process. This modeling leads to the minimization of an objective function. Conjugate Gradient algorithm (CG) is one of the best known optimization techniques. This paper proposes the use of the Conjugate Gradient algorithm (CG) for image segmentation, based on the Hidden Markov Random Field. Since derivatives are not available for this expression, finite differences are used in the CG algorithm to approximate the first derivative. The approach is evaluated using a number of publicly available images, where ground truth is known. The Dice Coefficient is used as an objective criterion to measure the quality of segmentation. The results show that the proposed CG approach compares favorably with other variants of Hidden Markov Random Field segmentation algorithms.