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

Yudong Zhang¹  Zhengchao Dong³  Shuihua Wang¹  Genlin Ji¹  Jiquan Yang² 

[1] School of Computer Science and Technology, Nanjing Normal University, Nanjing, Jiangsu 210023, China; E-Mails:;Jiangsu Key Laboratory of 3D Printing Equipment and Manufacturing, Nanjing, Jiangsu 210042, China; E-Mail:;Translational Imaging Division & MRI Unit, Columbia University and New York State Psychiatric Institute, New York, NY 10032, USA; E-Mail:

关键词: Shannon entropy;    Tsallis entropy;    magnetic resonance imaging;    computer-aided diagnosis;    discrete wavelet packet transform;    support vector machine;    kernel technique;    pattern recognition;    classification;   

DOI  :  10.3390/e17041795

来源: mdpi

【 摘 要 】

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.

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 proximal 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.

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.

We have proved the effectiveness of the proposed method, which achieved nearly 100% accuracy over three benchmark datasets.

【 授权许可】

CC BY   
© 2015 by the authors; licensee MDPI, Basel, Switzerland