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    STUDIA INFORMATICA - Issue no. 1 / 2009  
         
  Article:   A HYBRID TECHNIQUE FOR AUTOMATIC MRI BRAIN IMAGES CLASSIFICATION.

Authors:  EL-SAYED A. EL-DAHSHAN, ABDEL-BADEEH M. SALEM, TAMER H. YOUNIS. 		 
       
         
  Abstract:  This paper presents two hybrid techniques for the classification of the magnetic resonance human brain images. The proposed hybrid technique consists of three stages, namely, feature extraction, dimensionality reduction, and classification. In the first stage, we have obtained the features related with MRI images using discrete wavelet transformation (DWT). In the second stage, the features of magnetic resonance images (MRI) have been reduced using principles component analysis (PCA) to the more essential features. In the classification stage, two classifiers based on supervised machine learning have been developed. The first classifier based on feed forward back-propagation artificial neural network (FP-ANN) and the second classifier based on k-nearest neighbor (k-NN). The classifiers have been used to classify subjects as normal or abnormal MRI human images. A classification with a success of 95.6% and 98.6% has been obtained by the two proposed classifiers FP-ANN and k-NN respectively. This result shows that the proposed hybrid techniques are robust and effective compared with other recently work.

Key words and phrases. MRI human brain Images; Wavelet Transformation (WT); Principle Components Analysis (PCA); Feedforawd-Backpropagation Neural Network (FP-ANN); k-Nearest Neighbors, Classification.


  
         
     
         
         
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