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Comparative Experiments of Brain Tumor Segmentation Methods in MRI image

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dc.contributor.author Min, Aye
dc.contributor.author War, Nu
dc.date.accessioned 2019-07-23T04:44:53Z
dc.date.available 2019-07-23T04:44:53Z
dc.date.issued 2019-02-27
dc.identifier.uri http://onlineresource.ucsy.edu.mm/handle/123456789/1224
dc.description.abstract The detection of brain cancer without human interfering is a major problem in the domain of medicinal image processing. The segmentation of brain images of MRI is a technique used as a first step to extract different characteristics of these images for analysis, appreciative and understanding. The main function of brain segmentation by MRI is to detect the type of brain abnormality. Many segmentation techniques are proposed in the literature. In this comparative paper, we will discuss the behaviors of tested segmentation methods. Otsu thresholding, Region growing, Particle swarm optimization and Interactive graph cut segmentation methods are analyzed and compared in this paper. After segmented with these methods, the morphological operation is used to get exact shape and size of tumors. As a benchmark dataset, BRATS dataset is used to test segmentation results. en_US
dc.language.iso en en_US
dc.publisher Seventeenth International Conference on Computer Applications(ICCA 2019) en_US
dc.title Comparative Experiments of Brain Tumor Segmentation Methods in MRI image en_US
dc.type Article en_US


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