Combination of Hidden Markov Random Field and Conjugate Gradient for Brain Image Segmentation

TL;DR

This paper introduces a novel image segmentation method combining Hidden Markov Random Fields with the Conjugate Gradient algorithm, improving accuracy in medical image analysis by optimizing segmentation quality.

Contribution

It presents a new approach integrating Conjugate Gradient optimization with Hidden Markov Random Fields for enhanced brain image segmentation.

Findings

The proposed method achieves higher Dice Coefficient scores.

It outperforms existing Hidden Markov Random Field segmentation variants.

The approach is validated on publicly available images with known ground truth.

Abstract

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.