Search results for "Broyden–Fletcher–Goldfarb–Shanno algorithm"

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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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Hidden Markov Random Field model and BFGS algorithm for Brain Image Segmentation

2016

Brain MR images segmentation has attracted a particular focus in medical imaging. The automatic image analysis and interpretation became a necessity. Segmentation is one of the key operations to provide a crucial decision support to physicians. Its goal is to simplify the representation of an image into items meaningful and easier to analyze. Hidden Markov Random Fields (HMRF) provide an elegant way to model the segmentation problem. This model leads to the minimization problem of a function. BFGS (Broyden-Fletcher-Goldfarb-Shanno algorithm) is one of the most powerful methods to solve unconstrained optimization problem. This paper presents how we combine HMRF and BFGS to achieve a good seg…

business.industrySegmentation-based object categorizationComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISIONScale-space segmentationPattern recognitionImage segmentationMachine learningcomputer.software_genreSørensen–Dice coefficientBroyden–Fletcher–Goldfarb–Shanno algorithmSegmentationArtificial intelligenceHidden Markov random fieldbusinessHidden Markov modelcomputerMathematicsProceedings of the Mediterranean Conference on Pattern Recognition and Artificial Intelligence
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