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

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• 作者：Xingxing Zhou (20)
  Shuihua Wang (21)
  Wei Xu (22)
  Genlin Ji (20)
  Preetha Phillips (23)
  Ping Sun (24)
  Yudong Zhang (20)
  
  20. School of Computer Science and Technology ; Nanjing Normal University ; Nanjing ; Jiangsu ; 210023 ; China
  21. School of Electronic Science and Engineering ; Nanjing University ; Nanjing ; Jiangsu ; 210046 ; China
  22. Student Affairs Office ; Nanjing Institute of Industry Technology ; Nanjing ; Jiangsu ; 210023 ; China
  23. School of Natural Sciences and Mathematics ; Shepherd University ; Shepherdstown ; West Virginia ; 25443 ; USA
  24. Department of Electrical Engineering ; The City College of New York ; CUNY ; New York ; NY ; 10031 ; USA
  
• 关键词：Wavelet transform ; Entropy ; Na茂ve Bayes classifier ; Classification
• 刊名：Lecture Notes in Computer Science
• 出版年：2015
• 出版时间：2015
• 年：2015
• 卷：9043
• 期：1
• 页码：201-209
• 全文大小：331 KB
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• 作者单位：Bioinformatics and Biomedical Engineering
• 丛书名：978-3-319-16482-3
• 刊物类别：Computer Science
• 刊物主题：Artificial Intelligence and Robotics
  Computer Communication Networks
  Software Engineering
  Data Encryption
  Database Management
  Computation by Abstract Devices
  Algorithm Analysis and Problem Complexity
  
• 出版者：Springer Berlin / Heidelberg
• ISSN：1611-3349

文摘

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.