Skip to Main content Skip to Navigation
Conference papers

Conjugate Gradient Method for Brain Magnetic Resonance Images Segmentation

Abstract : Image segmentation is the process of partitioning the image into regions of interest in order to provide a meaningful representation of information. 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 nonlinear Conjugate Gradient algorithm (CG) for image segmentation, in combination with the Hidden Markov Random Field modelization. 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.
Document type :
Conference papers
Complete list of metadata

Cited literature [44 references]  Display  Hide  Download
Contributor : Hal Ifip Connect in order to contact the contributor
Submitted on : Tuesday, November 6, 2018 - 5:18:32 PM
Last modification on : Wednesday, November 3, 2021 - 5:32:38 AM
Long-term archiving on: : Thursday, February 7, 2019 - 4:37:56 PM


Files produced by the author(s)


Distributed under a Creative Commons Attribution 4.0 International License



El-Hachemi Guerrout, Samy Ait-Aoudia, Dominique Michelucci, Ramdane Mahiou. Conjugate Gradient Method for Brain Magnetic Resonance Images Segmentation. 6th IFIP International Conference on Computational Intelligence and Its Applications (CIIA), May 2018, Oran, Algeria. pp.561-572, ⟨10.1007/978-3-319-89743-1_48⟩. ⟨hal-01913885⟩



Les métriques sont temporairement indisponibles