Automatic Diagnosis of Brain Magnetic Resonance Images based on Riemannian Geometry

Mohamed Gouskir, Belaid Bouikhalene, Hicham Aissaoui and Benachir Elhadadi
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Mohamed Gouskir: SD Laboratory, Faculty of Sience and Technology, Université Sultan Moulay Slimane, Beni Mellal, Morocco
Belaid Bouikhalene: LIRST Laboratory, Université Sultan Moulay Slimane, Beni Mellal, Morocco
Hicham Aissaoui: SD Laboratory, Faculty of Sience and Technology, Université Sultan Moulay Slimane, Beni Mellal, Morocco
Benachir Elhadadi: SD Laboratory, Faculty of Sience and Technology, Université Sultan Moulay Slimane, Beni Mellal, Morocco

Journal of Electronic Commerce in Organizations (JECO), 2015, vol. 13, issue 2, 30-40

Abstract: Automated brain tumor detection and segmentation, from medical images, is one of the most challenging. The authors present, in this paper, an automatic diagnosis of brain magnetic resonance image. The goal is to prepare the image of the human brain to locate the existence of abnormal tissues in multi-modal brain magnetic resonance images. The authors start from the image acquisition, reduce information, brain extraction, and then brain region diagnosis. Brain extraction is the most important preprocessing step for automatic brain image analysis. The authors consider the image as residing in a Riemannian space and they based on Riemannian manifold to develop an algorithm to extract brain regions, these regions used in other algorithm to brain tumor detection, segmentation and classification. Riemannian Manifolds show the efficient results to brain extraction and brain analysis for multi-modal resonance magnetic images.

Date: 2015
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Persistent link: https://EconPapers.repec.org/RePEc:igg:jeco00:v:13:y:2015:i:2:p:30-40

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