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Hidden Markov random field model and Broyden–Fletcher–Goldfarb–Shanno algorithm for brain image segmentation

Abstract : Many routine medical examinations produce images of patients suffering from various pathologies. With the huge number of medical images, the manual analysis and interpretation became a tedious task. Thus, automatic image segmentation became essential for diagnosis assistance. Segmentation consists in dividing the image into homogeneous and significant regions. We focus on hidden Markov random fields referred to as HMRF to model the problem of segmentation. This modelisation leads to a classical function minimisation problem. Broyden-Fletcher-Goldfarb-Shanno algorithm referred to as BFGS is one of the most powerful methods to solve unconstrained optimisation problem. In this paper, we investigate the combination of HMRF and BFGS algorithm to perform the segmentation operation. The proposed method shows very good segmentation results comparing with well-known approaches. The tests are conducted on brain magnetic resonance image databases (BrainWeb and IBSR) largely used to objectively confront the results obtained. The well-known Dice coefficient (DC) was used as similarity metric. The experimental results show that, in many cases, our proposed method approaches the perfect segmentation with a Dice Coefficient above .9. Moreover, it generally outperforms other methods in the tests conducted.
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Contributeur : LE2I - université de Bourgogne Connectez-vous pour contacter le contributeur
Soumis le : vendredi 24 août 2018 - 16:43:48
Dernière modification le : vendredi 5 août 2022 - 14:54:00



El-Hachemi Guerrout, Samy Ait-Aoudia, Dominique Michelucci, Ramdane Mahiou. Hidden Markov random field model and Broyden–Fletcher–Goldfarb–Shanno algorithm for brain image segmentation. Journal of Experimental and Theoretical Artificial Intelligence, 2018, 30 (3), pp.415 - 427. ⟨10.1080/0952813X.2017.1409280⟩. ⟨hal-01861654⟩



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