Automated Feature Extraction in Brain Tumor by Magnetic Resonance Imaging Using Gaussian Mixture Models
This paper presents a novel method for Glioblastoma (GBM) feature extraction based on Gaussian mixture model (GMM) features using MRI. We addressed the task of the new features to identify GBM using T1 and T2 weighted images (T1-WI, T2-WI) and Fluid-Attenuated Inversion Recovery (FLAIR) MR images. A...
Main Author: | |
---|---|
Format: | Article |
Language: | English |
Published: |
Hindawi Limited
2015-01-01
|
Series: | International Journal of Biomedical Imaging |
Online Access: | http://dx.doi.org/10.1155/2015/868031 |
id |
doaj-c7a5bc0f6b444afab7a8ffcddb635886 |
---|---|
record_format |
Article |
spelling |
doaj-c7a5bc0f6b444afab7a8ffcddb6358862020-11-24T23:02:08ZengHindawi LimitedInternational Journal of Biomedical Imaging1687-41881687-41962015-01-01201510.1155/2015/868031868031Automated Feature Extraction in Brain Tumor by Magnetic Resonance Imaging Using Gaussian Mixture ModelsAhmad Chaddad0Department of Diagnostic Radiology, University of Texas MD Anderson Cancer Center, 1400 Pressler Street, Houston, TX 77030, USAThis paper presents a novel method for Glioblastoma (GBM) feature extraction based on Gaussian mixture model (GMM) features using MRI. We addressed the task of the new features to identify GBM using T1 and T2 weighted images (T1-WI, T2-WI) and Fluid-Attenuated Inversion Recovery (FLAIR) MR images. A pathologic area was detected using multithresholding segmentation with morphological operations of MR images. Multiclassifier techniques were considered to evaluate the performance of the feature based scheme in terms of its capability to discriminate GBM and normal tissue. GMM features demonstrated the best performance by the comparative study using principal component analysis (PCA) and wavelet based features. For the T1-WI, the accuracy performance was 97.05% (AUC = 92.73%) with 0.00% missed detection and 2.95% false alarm. In the T2-WI, the same accuracy (97.05%, AUC = 91.70%) value was achieved with 2.95% missed detection and 0.00% false alarm. In FLAIR mode the accuracy decreased to 94.11% (AUC = 95.85%) with 0.00% missed detection and 5.89% false alarm. These experimental results are promising to enhance the characteristics of heterogeneity and hence early treatment of GBM.http://dx.doi.org/10.1155/2015/868031 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Ahmad Chaddad |
spellingShingle |
Ahmad Chaddad Automated Feature Extraction in Brain Tumor by Magnetic Resonance Imaging Using Gaussian Mixture Models International Journal of Biomedical Imaging |
author_facet |
Ahmad Chaddad |
author_sort |
Ahmad Chaddad |
title |
Automated Feature Extraction in Brain Tumor by Magnetic Resonance Imaging Using Gaussian Mixture Models |
title_short |
Automated Feature Extraction in Brain Tumor by Magnetic Resonance Imaging Using Gaussian Mixture Models |
title_full |
Automated Feature Extraction in Brain Tumor by Magnetic Resonance Imaging Using Gaussian Mixture Models |
title_fullStr |
Automated Feature Extraction in Brain Tumor by Magnetic Resonance Imaging Using Gaussian Mixture Models |
title_full_unstemmed |
Automated Feature Extraction in Brain Tumor by Magnetic Resonance Imaging Using Gaussian Mixture Models |
title_sort |
automated feature extraction in brain tumor by magnetic resonance imaging using gaussian mixture models |
publisher |
Hindawi Limited |
series |
International Journal of Biomedical Imaging |
issn |
1687-4188 1687-4196 |
publishDate |
2015-01-01 |
description |
This paper presents a novel method for Glioblastoma (GBM) feature extraction based on Gaussian mixture model (GMM) features using MRI. We addressed the task of the new features to identify GBM using T1 and T2 weighted images (T1-WI, T2-WI) and Fluid-Attenuated Inversion Recovery (FLAIR) MR images. A pathologic area was detected using multithresholding segmentation with morphological operations of MR images. Multiclassifier techniques were considered to evaluate the performance of the feature based scheme in terms of its capability to discriminate GBM and normal tissue. GMM features demonstrated the best performance by the comparative study using principal component analysis (PCA) and wavelet based features. For the T1-WI, the accuracy performance was 97.05% (AUC = 92.73%) with 0.00% missed detection and 2.95% false alarm. In the T2-WI, the same accuracy (97.05%, AUC = 91.70%) value was achieved with 2.95% missed detection and 0.00% false alarm. In FLAIR mode the accuracy decreased to 94.11% (AUC = 95.85%) with 0.00% missed detection and 5.89% false alarm. These experimental results are promising to enhance the characteristics of heterogeneity and hence early treatment of GBM. |
url |
http://dx.doi.org/10.1155/2015/868031 |
work_keys_str_mv |
AT ahmadchaddad automatedfeatureextractioninbraintumorbymagneticresonanceimagingusinggaussianmixturemodels |
_version_ |
1725637218631942144 |