Next Article in Journal
Geometry of Fisher Information Metric and the Barycenter Map
Next Article in Special Issue
Operational Reliability Assessment of Compressor Gearboxes with Normalized Lifting Wavelet Entropy from Condition Monitoring Information
Previous Article in Journal
Multidimensional Scaling Visualization Using Parametric Similarity Indices
Open AccessArticle

Preclinical Diagnosis of Magnetic Resonance (MR) Brain Images via Discrete Wavelet Packet Transform with Tsallis Entropy and Generalized Eigenvalue Proximal Support Vector Machine (GEPSVM)

1
School of Computer Science and Technology, Nanjing Normal University, Nanjing, Jiangsu 210023, China
2
Translational Imaging Division & MRI Unit, Columbia University and New York State Psychiatric Institute, New York, NY 10032, USA
3
School of Electronic Science and Engineering, Nanjing University, Nanjing, Jiangsu 210046, China
4
Jiangsu Key Laboratory of 3D Printing Equipment and Manufacturing, Nanjing, Jiangsu 210042, China
*
Author to whom correspondence should be addressed.
Academic Editor: Raúl Alcaraz Martínez
Entropy 2015, 17(4), 1795-1813; https://doi.org/10.3390/e17041795
Received: 23 December 2014 / Revised: 23 March 2015 / Accepted: 26 March 2015 / Published: 30 March 2015
(This article belongs to the Special Issue Wavelet Entropy: Computation and Applications)
Background: Developing an accurate computer-aided diagnosis (CAD) system of MR brain images is essential for medical interpretation and analysis. In this study, we propose a novel automatic CAD system to distinguish abnormal brains from normal brains in MRI scanning. Methods: The proposed method simplifies the task to a binary classification problem. We used discrete wavelet packet transform (DWPT) to extract wavelet packet coefficients from MR brain images. Next, Shannon entropy (SE) and Tsallis entropy (TE) were harnessed to obtain entropy features from DWPT coefficients. Finally, generalized eigenvalue proximate support vector machine (GEPSVM), and GEPSVM with radial basis function (RBF) kernel, were employed as classifier. We tested the four proposed diagnosis methods (DWPT + SE + GEPSVM, DWPT + TE + GEPSVM, DWPT + SE + GEPSVM + RBF, and DWPT + TE + GEPSVM + RBF) on three benchmark datasets of Dataset-66, Dataset-160, and Dataset-255. Results: The 10 repetition of K-fold stratified cross validation results showed the proposed DWPT + TE + GEPSVM + RBF method excelled not only other three proposed classifiers but also existing state-of-the-art methods in terms of classification accuracy. In addition, the DWPT + TE + GEPSVM + RBF method achieved accuracy of 100%, 100%, and 99.53% on Dataset-66, Dataset-160, and Dataset-255, respectively. For Dataset-255, the offline learning cost 8.4430s and online prediction cost merely 0.1059s. Conclusions: We have proved the effectiveness of the proposed method, which achieved nearly 100% accuracy over three benchmark datasets. View Full-Text
Keywords: Shannon entropy; Tsallis entropy; magnetic resonance imaging; computer-aided diagnosis; discrete wavelet packet transform; support vector machine; kernel technique; pattern recognition; classification Shannon entropy; Tsallis entropy; magnetic resonance imaging; computer-aided diagnosis; discrete wavelet packet transform; support vector machine; kernel technique; pattern recognition; classification
MDPI and ACS Style

Zhang, Y.; Dong, Z.; Wang, S.; Ji, G.; Yang, J. Preclinical Diagnosis of Magnetic Resonance (MR) Brain Images via Discrete Wavelet Packet Transform with Tsallis Entropy and Generalized Eigenvalue Proximal Support Vector Machine (GEPSVM). Entropy 2015, 17, 1795-1813.

Show more citation formats Show less citations formats

Article Access Map by Country/Region

1
Only visits after 24 November 2015 are recorded.
Back to TopTop