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Multiple sclerosis diagnosis based on analysis of subbands of 2-D wavelet transform applied on MR-images

Torabi, M ; Sharif University of Technology | 2007

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  1. Type of Document: Article
  2. DOI: 10.1109/AICCSA.2007.370711
  3. Publisher: 2007
  4. Abstract:
  5. In this study, we have proposed a novel approach to investigate the features of four subbands of 2-D wavelet transform in magnetic resonance images (MRIs) for normal and abnormal brains which defected by Multiple Sclerosis (MS). Concurrently, another method extracts different kinds of features in spatial domain. Totally, 116 features have been extracted. Before applying the algorithm, we have to use a registration method because of variety in size of brain images. All extracted features have been passed over the Principal Component Analysis (PCA) and have been pushed to an Artificial Neural Network (ANN) that is a feed-forward type. According to changing in position of defected parts of brain, we have analyzed four different MRI datasets in different stages of MS progression, including 101 MRIs of normal and abnormal brain images. In all cases, certain diagnosis is gained. Meantime, 40 percent of the datasets have been reserved as the "test data ". © 2007 IEEE
  6. Keywords:
  7. Brain images ; Multiple sclerosis diagnosis ; Brain ; Feedforward neural networks ; Image reconstruction ; Magnetic resonance imaging ; Medical imaging ; Principal component analysis ; Wavelet transforms ; Feature extraction
  8. Source: 2007 IEEE/ACS International Conference on Computer Systems and Applications, AICCSA 2007, Amman, 13 May 2007 through 16 May 2007 ; 2007 , Pages 717-721 ; 1424410312 (ISBN); 9781424410316 (ISBN)
  9. URL: https://ieeexplore.ieee.org/document/4231039