Madina Hamiane, Fatema Saeed Salman



MRI Brain Image Analysis and Classification for Computer-Assisted Diagnosis

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Magnetic Resonance Imaging is a powerful technique that helps in the diagnosis of various medical conditions. Detection of brain abnormalities, such as brain tumors, in brain MRI images are considered in this work. These images are often corrupted by noise from various sources. In this work, MRI brain images with various abnormalities are pre-processed, enhanced , then classified to yield an efficient diagnosis tool that could help medical practitioners in identifying abnormal brain lesions . The work is based on the use of the Discrete Wavelet Transforms (DWT) along with thresholding techniques for efficient noise removal, followed by edge detection and threshold segmentation of the denoised images prior to the extraction of the enhanced image features through the use of morphological operations. The images are finally classified using a Support Vector scheme with a radial basis function kernel. The performance of the classifier is evaluated and the results of the classification show that the proposed scheme accurately distinguishes normal brain images from those with abnormal lesions. The accuracy of the classification is shown to be 100% which is by far superior to the results reported in the literature



MRI Brain Image Processing, Image segmentation, Image Feature Extraction, Gray-Level Cooccurrence Matrix, Discrete Wavelet Transform, RBF Support Vector Machine



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Cite this paper

Madina Hamiane, Fatema Saeed Salman. (2017) MRI Brain Image Analysis and Classification for Computer-Assisted Diagnosis. International Journal of Economics and Management Systems, 2, 229-236


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