MRI Brain Image Classification Using Haar Wavelet and Artificial Neural Network

Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 325)

Abstract

A combined approach with MRI brain image denoising and abnormality detection process is proposed in this paper. The proposed technique is comprised of three stages, namely (i) image preprocessing, (ii) feature extraction, and (iii) image classification. Initially, in the preprocessing stage, denoising is performed on the input brain MRI image. The denoising process on the input image increases the accuracy of feature extraction stage. In feature extraction phase, the image features such as mean, variance, and multilevel 2D Haar wavelet decomposition are extracted for classifying the images in the database into normal and abnormal. By using these extracted features, the MRI brain images are classified by the well-known classification technique such as feed forward back propagation neural networks (FFBNN). The implementation of the proposed method shows improvements in classification of MRI images.

Keywords

Image preprocessing Feature extraction Image classification Feed forward back propagation neural networks 

References

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Copyright information

© Springer India 2015

Authors and Affiliations

  1. 1.Department of Computer Science and EngineeringLourdes Matha College of Science and TechnologyThiruvananthapuramIndia
  2. 2.Department of Electronics and CommunicationTKM College of EngineeringKollamIndia

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