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- W166286719 abstract "Segmentation of Magnetic Resonance Imaging (MRI) images is the most challenging problems in medical imaging. This paper contrasts the performances of Adaptive Network-Based Fuzzy Inference System (ANFIS), k-Nearest Neighbors (k-NN) and Fuzzy c-Means (FCM) in brain abnormalities segmentation. Preliminary data analysis is performed to analyze the characteristics for all brain components by extracting the minimum, maximum and mean grey level pixel values. The segmentation performances of each technique is tested to hundred and twenty controlled testing data which designed by cutting various shapes and size of various abnormalities and pasting it onto normal brain tissues. The tissues are divided into three categories of low, medium and high based on the grey level pixel value intensities. ANFIS proves to return the best segmentation performances in light abnormalities, whereas the k-NN on the other hand performed well in dark abnormalities segmentation." @default.
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- W166286719 date "2011-07-15" @default.
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- W166286719 title "Brain abnormalities segmentation performances contrasting: adaptive network-based fuzzy inference system (ANFIS) vs K-nearest neighbors (k-NN) vs fuzzy c-means (FCM)" @default.
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