A hybrid method for MRI brain image classification

Yudong Zhang*, Zhengchao Dong, Lenan Wu, Shuihua Wang

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

295 Citations (Scopus)

Abstract

Automated and accurate classification of MR brain images is of importance for the analysis and interpretation of these images and many methods have been proposed. In this paper, we present a neural network (NN) based method to classify a given MR brain image as normal or abnormal. This method first employs wavelet transform to extract features from images, and then applies the technique of principle component analysis (PCA) to reduce the dimensions of features. The reduced features are sent to a back propagation (BP) NN, with which scaled conjugate gradient (SCG) is adopted to find the optimal weights of the NN. We applied this method on 66 images (18 normal, 48 abnormal). The classification accuracies on both training and test images are 100%, and the computation time per image is only 0.0451 s.

Original languageEnglish
Pages (from-to)10049-10053
Number of pages5
JournalExpert Systems with Applications
Volume38
Issue number8
DOIs
Publication statusPublished - Aug 2011
Externally publishedYes

Keywords

  • Back propagation neural network
  • Magnetic resonance imaging
  • Principle component analysis
  • Wavelet transform

Fingerprint

Dive into the research topics of 'A hybrid method for MRI brain image classification'. Together they form a unique fingerprint.

Cite this