Search results for "Hidden Markov Random Field"

showing 5 items of 5 documents

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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Conjugate Gradient Method for Brain Magnetic Resonance Images Segmentation

2018

Part 8: Pattern Recognition and Image Processing; International audience; 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 Conjugat…

Ground truthComputer sciencebusiness.industryThe Conjugate Gradient algorithmComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISIONBrain image segmentationPattern recognition02 engineering and technologyImage segmentationImage (mathematics)Nonlinear conjugate gradient method03 medical and health sciences0302 clinical medicineDice Coefficient metricHidden Markov Random FieldConjugate gradient methodComputer Science::Computer Vision and Pattern Recognition0202 electrical engineering electronic engineering information engineering020201 artificial intelligence & image processingSegmentation[INFO]Computer Science [cs]Artificial intelligencebusinessHidden Markov random field030217 neurology & neurosurgery
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Hidden Markov Random Fields and Direct Search Methods for Medical Image Segmentation

2016

The goal of image segmentation is to simplify the representation of an image to items meaningful and easier to analyze. Medical image segmentation is one of the fundamental problems in image processing field. It aims to provide a crucial decision support to physicians. There is no one way to perform the segmentation. There are several methods based on HMRF. Hidden Markov Random Fields (HMRF) constitute an elegant way to model the problem of segmentation. This modelling leads to the minimization of an energy function. In this paper we investigate direct search methods that are Nelder-Mead and Torczon methods to solve this optimization problem. The quality of segmentation is evaluated on grou…

Segmentation-based object categorizationbusiness.industryComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISIONScale-space segmentationImage processing02 engineering and technologyImage segmentationMachine learningcomputer.software_genreSørensen–Dice coefficient0202 electrical engineering electronic engineering information engineering020201 artificial intelligence & image processingSegmentationArtificial intelligenceHidden Markov random fieldHidden Markov modelbusinesscomputerMathematicsProceedings of the 5th International Conference on Pattern Recognition Applications and Methods
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Spectral adaptation of hyperspectral flight lines using VHR contextual information

2014

Abstract: Due to technological constraints, hyperspectral earth observation imagery are often a mosaic of overlapping flight lines collected in different passes over the area of interest. This causes variations in aqcuisition conditions such that the reflected spectrum can vary significantly between these flight lines. Partly, this problem is solved by atmospherical correction, but residual spectral differences often remain. A probabilistic domain adaptation framework based on graph matching using Hidden Markov Random Fields was recently proposed for transforming hyperspectral data from one image to better correspond to the other. This paper investigates the use of scale and angle invariant…

VHR imageryHyperspectral imaginggraph matchingComputer sciencebusiness.industrydomain adaptationPhysicsHyperspectral imagingPattern recognitionFilter (signal processing)Rendering (computer graphics)Computer Science::Computer Vision and Pattern RecognitionFull spectral imagingtextural featuresComputer visionArtificial intelligenceHidden Markov random fieldHidden Markov modelbusiness
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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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