Detection of Pathological Brain in MRI Scanning Based on Wavelet-Entropy and Naive Bayes Classifier

  • Xingxing Zhou
  • Shuihua Wang
  • Wei Xu
  • Genlin Ji
  • Preetha Phillips
  • Ping Sun
  • Yudong Zhang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9043)


An accurate diagnosis is important for the medical treatment of patients suffered from brain disease. Nuclear magnetic resonance images are commonly used by technicians to assist the pre-clinical diagnosis, rating them by visual evaluations. The classification of NMR images of normal and pathological brains poses a challenge from technological point of view, since NMR imaging generates a large information set that reflects the conditions of the brain. In this work, we present a computer assisted diagnosis method based on a wavelet-entropy (In this paper 2D-discrete wavelet transform has been used, in that it can extract more information) of the feature space approach and a Naive Bayes classifier classification method for improving the brain diagnosis accuracy by means of NMR images. The most relevant image feature is selected as the wavelet entropy, which is used to train a Naive Bayes classifier. The results over 64 images show that the sensitivity of the classifier is as high as 94.50%, the specificity 91.70%, the overall accuracy 92.60%. It is easily observed from the data that the proposed classifier can detect abnormal brains from normal controls within excellent performance, which is competitive with latest existing methods.


Wavelet transform Entropy Naïve Bayes classifier Classification 


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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Xingxing Zhou
    • 1
  • Shuihua Wang
    • 2
  • Wei Xu
    • 3
  • Genlin Ji
    • 1
  • Preetha Phillips
    • 4
  • Ping Sun
    • 5
  • Yudong Zhang
    • 1
  1. 1.School of Computer Science and TechnologyNanjing Normal UniversityNanjingChina
  2. 2.School of Electronic Science and EngineeringNanjing UniversityNanjingChina
  3. 3.Student Affairs OfficeNanjing Institute of Industry TechnologyNanjingChina
  4. 4.School of Natural Sciences and MathematicsShepherd UniversityShepherdstownUSA
  5. 5.Department of Electrical EngineeringThe City College of New York, CUNYNew YorkUSA

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