Discrete Wavelet Transform Based Whole-Spectral and Sub-Spectral Analysis for Improved Brain Tumour Clustering using Single Voxel MR Spectroscopy
File(s)
Accepted version
Author(s)
Yang, G
Nawaz, T
Barrick, T
Howe, F
Slabaugh, G
Type
Journal Article
Abstract
Many approaches have been considered for automatic grading of brain tumours by means of pattern recognition with magnetic resonance spectroscopy (MRS). Providing an improved technique which can assist clinicians in accurately identifying brain tumour grades is our main objective. The proposed technique, which is based on the discrete wavelet transform (DWT) of whole-spectral or sub-spectral information of key metabolites, combined with unsupervised learning, inspects the separability of the extracted wavelet features from the MRS signal to aid the clustering. In total, we included 134 short echo time single voxel MRS spectra (SV MRS) in our study that cover normal controls, low grade and high grade tumours. The combination of DWT-based whole-spectral or sub-spectral analysis and unsupervised clustering achieved an overall clustering accuracy of 94.8% and a balanced error rate of 7.8%. To the best of our knowledge, it is the first study using DWT combined with unsupervised learning to cluster brain SV MRS. Instead of dimensionality reduction on SV MRS or feature selection using model fitting, our study provides an alternative method of extracting features to obtain promising clustering results.
Date Issued
2015-06-22
Date Acceptance
2015-06-22
Citation
IEEE Transactions on Biomedical Engineering, 2015, 62 (12), pp.2860-2866
ISSN
0018-9294
Publisher
IEEE
Start Page
2860
End Page
2866
Journal / Book Title
IEEE Transactions on Biomedical Engineering
Volume
62
Issue
12
Copyright Statement
© 2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Subjects
Biomedical Engineering
0903 Biomedical Engineering
0906 Electrical And Electronic Engineering
0801 Artificial Intelligence And Image Processing
Publication Status
Published