Search results for "MR-images"

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

2018

International audience; 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. …

Dice coefficient criterionComputer scienceBrain image segmentation02 engineering and technologyMR-images[INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]Theoretical Computer Science03 medical and health sciences0302 clinical medicineArtificial Intelligence0202 electrical engineering electronic engineering information engineering[INFO]Computer Science [cs]SegmentationBrain magnetic resonance imagingHidden Markov modelRandom fieldbusiness.industryBroyden-Fletcher-Goldfarb-Shanno algorithmPattern recognitionImage segmentationhidden Markov random fieldMinimization3. Good healthHomogeneousBroyden–Fletcher–Goldfarb–Shanno algorithm020201 artificial intelligence & image processingAutomatic segmentationArtificial intelligenceHidden Markov random fieldbusiness030217 neurology & neurosurgerySoftwareJournal of Experimental & Theoretical Artificial Intelligence
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