Detection of Pathological Brain in MRI Scanning Based on Wavelet-Entropy and Naive Bayes Classifier
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.
KeywordsWavelet transform Entropy Naïve Bayes classifier Classification
Unable to display preview. Download preview PDF.
- 6.Schneider, M.F., Krick, C.M., Retz, W., Hengesch, G., Retz-Junginger, P., Reith, W., Rösler, M.: Impairment of fronto-striatal and parietal cerebral networks correlates with attention deficit hyperactivity disorder (ADHD) psychopathology in adults – A functional magnetic resonance imaging (fMRI) study. Psychiatry Research: Neuroimaging 183, 75–84 (2010)CrossRefGoogle Scholar
- 8.Jero, S.E., Ramu, P., Ramakrishnan, S.: Discrete Wavelet Transform and Singular Value Decomposition Based ECG Steganography for Secured Patient Information Transmission. Journal of Medical Systems 38 (2014)Google Scholar
- 11.Frantzidis, C.A., Vivas, A.B., Tsolaki, A., Klados, M.A., Tsolaki, M., Bamidis, P.D.: Functional disorganization of small-world brain networks in mild Alzheimer’s Disease and amnestic Mild Cognitive Impairment: an EEG study using Relative Wavelet Entropy (RWE). Frontiers in aging neuroscience 6 (2014)Google Scholar
- 14.Kotsiantis, S.: Integrating Global and Local Application of Naive Bayes Classifier. International Arab Journal of Information Technology 11, 300–307 (2014)Google Scholar
- 18.Darji, A.D., Kushwah, S.S., Merchant, S.N., Chandorkar, A.N.: High-performance hardware architectures for multi-level lifting-based discrete wavelet transform. Eurasip Journal on Image and Video Processing (2014)Google Scholar
- 19.Ganesan, K., Acharya, U.R., Chua, C.K., Min, L.C., Abraham, T.K.: Automated Diagnosis of Mammogram Images of Breast Cancer Using Discrete Wavelet Transform and Spherical Wavelet Transform Features: A Comparative Study. Technology in Cancer Research & Treatment 13, 605–615 (2014)Google Scholar
- 24.Sheppard, S., Nathan, D., Zhu, L.J.: Accurate identification of polyadenylation sites from 3 ’ end deep sequencing using a naive Bayes classifier 29, 2564 (2013), Bioinformatics (Oxford, England) 30, 596–596 (2014) Google Scholar
- 28.Zhang, Y., Wang, S., Ji, G., Dong, Z.: An MR Brain Images Classifier System via Particle Swarm Optimization and Kernel Support Vector Machine. The Scientific World Journal 2013, 9 (2013)Google Scholar