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In this paper we report the set-up and results of the Multimodal Brain Tumor Image Segmentation Benchmark (BRATS) organized in conjunction with the MICCAI 2012 and 2013 conferences. Twenty state-of-the-art tumor segmentation algorithms were applied to a set of 65 multi-contrast MR scans of low- and high-grade glioma patients-manually annotated by up to four(More)
A stochastic model for characterizing tumor texture in brain magnetic resonance (MR) images is proposed. The efficacy of the model is demonstrated in patient-independent brain tumor texture feature extraction and tumor segmentation in magnetic resonance images (MRIs). Due to complex appearance in MRI, brain tumor texture is formulated using a(More)
To detect the tumor in the brain is very important task but the major problem occurred is that its very time consuming. We provide an approach towards the automation of this process in this paper. We take magnetic resonance images of the brain as a input and attempt to calculated the position and the size of the tumor. Each pixel in each slice will be(More)
In the last few years, many image processing techniques have been presented in order to perform different brain tumor detection tasks. These cover Content-Based Retrieval Technique, Component Labelling Algorithm, Fuzzy C-Mean Algorithm. There is fast growth of image processing available in last few years; image segmentation have work together to get(More)
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