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Design a Classification System for Brain Magnetic Resonance Image | ||
| Journal of University of Babylon | ||
| Article 1, Volume 21, Issue 8, 2013, Pages 2682-2689 | ||
| Authors | ||
| Hussein Attya Lafta; Esraa Abdullah Hussein | ||
| Abstract | ||
| Automated and accurate classification of brain MRI is such important that leads us to present a new robust classification technique for analyzing magnetic resonance images[Chris 2003]. In this work , the proposed method consist of three stages collection of images, feature extraction , and classification . We are used gray-level co-occurrence matrix (GLCM) is used to extract features from brain MRI . These features are given as input to k-nearest neighbor( K-NN) classifier to classify images as normal or abnormal brain MRI . | ||
| Keywords | ||
| Brain MRI; Feature extraction; GLCM | ||
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