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Computational intelligence techniques for human brain MRI classification
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DOI：10.1002/ima.22265.png)
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Abstract
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This study proposes an image classification methodology that automatically classifies human brain magnetic resonance (MR) images. The proposed methods contain four main stages: Data acquisition, preprocessing, feature extraction, feature reduction and classification, followed by evaluation. First stage starts by collecting MRI images from Harvard and our constructed Egyptian database. Second stage starts with noise reduction in MR images. Third stage obtains the features related to MRI images, using stationary wavelet transformation. In the fourth stage, the features of MR images have been reduced using principles of component analysis and kernel linear discriminator analysis (KLDA) to the more essential features. In last stage, the classification stage, two classifiers have been developed to classify subjects as normal or abnormal MRI human images. The first classifier is based on K-Nearest Neighbor (KNN) on Euclidean distance. The second classifier is based on Levenberg-Marquardt (LM-ANN). Classification accuracy of 100% for KNN and LM-ANN classifiers has been obtained. The result shows that the proposed methodologies are robust and effective compared with other recent works.
Keywords:
classification
computational intelligent techniques
feature extraction and reduction
MRI images
stationary wavelet transform
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