Next Article in Journal
Health Condition Evaluation for a Shearer through the Integration of a Fuzzy Neural Network and Improved Particle Swarm Optimization Algorithm
Next Article in Special Issue
2D Gaze Estimation Based on Pupil-Glint Vector Using an Artificial Neural Network
Previous Article in Journal
Analytical Solution for Interference Fit for Multi-Layer Thick-Walled Cylinders and the Application in Crankshaft Bearing Design
Previous Article in Special Issue
Determination of Optimal Initial Weights of an Artificial Neural Network by Using the Harmony Search Algorithm: Application to Breakwater Armor Stones
Article Menu

Export Article

Open AccessArticle
Appl. Sci. 2016, 6(6), 169; doi:10.3390/app6060169

Dual-Tree Complex Wavelet Transform and Twin Support Vector Machine for Pathological Brain Detection

1,2,†
,
3,4,†
,
5,6
,
3
,
7,†
and
1,8,9,*
1
School of Computer Science and Technology & School of Psychology, Nanjing Normal University, Nanjing 210023, China
2
Key Laboratory of Statistical information technology and data mining, State Statistics Bureau, Chengdu 610225, China
3
Jiangsu Key Laboratory of 3D Printing Equipment and Manufacturing, Nanjing 210042, China
4
Key laboratory of symbolic computation and knowledge engineering of ministry of education, Jilin University, Changchun 130012, China
5
Translational Imaging Division, Columbia University, New York, NY 10032, USA
6
State Key Lab of CAD & CG, Zhejiang University, Hangzhou 310027, China
7
Department of Radiology, Nanjing Children’s Hospital, Nanjing Medical University, Nanjing 210008, China
8
Guangxi Key Laboratory of Manufacturing System & Advanced Manufacturing Technology, Guilin 541004, China
9
Department of Neurology, First Affiliated Hospital of Nanjing Medical University, Nanjing 210029, China
These authors contributed equally to this work.
*
Author to whom correspondence should be addressed.
Academic Editor: Christian Dawson
Received: 28 March 2016 / Accepted: 30 May 2016 / Published: 3 June 2016
(This article belongs to the Special Issue Applied Artificial Neural Network)
View Full-Text   |   Download PDF [3571 KB, uploaded 3 June 2016]   |  

Abstract

(Aim) Classification of brain images as pathological or healthy case is a key pre-clinical step for potential patients. Manual classification is irreproducible and unreliable. In this study, we aim to develop an automatic classification system of brain images in magnetic resonance imaging (MRI). (Method) Three datasets were downloaded from the Internet. Those images are of T2-weighted along axial plane with size of 256 × 256. We utilized an s-level decomposition on the basis of dual-tree complex wavelet transform (DTCWT), in order to obtain 12s “variance and entropy (VE)” features from each subband. Afterwards, we used support vector machine (SVM) and its two variants: the generalized eigenvalue proximal SVM (GEPSVM) and the twin SVM (TSVM), as the classifiers. In all, we proposed three novel approaches: DTCWT + VE + SVM, DTCWT + VE + GEPSVM, and DTCWT + VE + TSVM. (Results) The results showed that our “DTCWT + VE + TSVM” obtained an average accuracy of 99.57%, which was not only better than the two other proposed methods, but also superior to 12 state-of-the-art approaches. In addition, parameter estimation showed the classification accuracy achieved the largest when the decomposition level s was assigned with a value of 1. Further, we used 100 slices from real subjects, and we found our proposed method was superior to human reports from neuroradiologists. (Conclusions) This proposed system is effective and feasible. View Full-Text
Keywords: magnetic resonance imaging; parameter estimation; support vector machine; dual-tree complex wavelet transform; twin support vector machine; variance; entropy magnetic resonance imaging; parameter estimation; support vector machine; dual-tree complex wavelet transform; twin support vector machine; variance; entropy
Figures

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Wang, S.; Lu, S.; Dong, Z.; Yang, J.; Yang, M.; Zhang, Y. Dual-Tree Complex Wavelet Transform and Twin Support Vector Machine for Pathological Brain Detection. Appl. Sci. 2016, 6, 169.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Appl. Sci. EISSN 2076-3417 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top