Brain abnormalities segmentation performances contrasting: adaptive network-based fuzzy inference system (ANFIS) vs K-nearest neighbors (k-NN) vs fuzzy c-means (FCM)

Authors:
Noor Elaiza Abdul Khalid;Shafaf Ibrahim;Mazani Manaf
Affiliations:
Faculty of Computer Science and Mathematics, University Technology MARA, Shah Alam, Selangor, Malaysia;Faculty of Computer Science and Mathematics, University Technology MARA, Shah Alam, Selangor, Malaysia;Faculty of Computer Science and Mathematics, University Technology MARA, Shah Alam, Selangor, Malaysia
Venue:
Proceedings of the 15th WSEAS international conference on Computers
Year:
2011

Citing 4
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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.