Enhanced Performance of Brain Tumor Classification via Tumor Region Augmentation and Partition.
Automatic classification of tissue types of region of interest (ROI) plays an important role in computer-aided diagnosis. In the current study, we focus on the classification of three types of brain tumors (i.e., meningioma, glioma, and pituitary tumor) in T1-weighted contrast-enhanced MRI (CE-MRI)...
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doaj-383b1d10cac34923a9dd976b90c248352020-11-25T02:12:58ZengPublic Library of Science (PLoS)PLoS ONE1932-62032015-01-011010e014038110.1371/journal.pone.0140381Enhanced Performance of Brain Tumor Classification via Tumor Region Augmentation and Partition.Jun ChengWei HuangShuangliang CaoRu YangWei YangZhaoqiang YunZhijian WangQianjin FengAutomatic classification of tissue types of region of interest (ROI) plays an important role in computer-aided diagnosis. In the current study, we focus on the classification of three types of brain tumors (i.e., meningioma, glioma, and pituitary tumor) in T1-weighted contrast-enhanced MRI (CE-MRI) images. Spatial pyramid matching (SPM), which splits the image into increasingly fine rectangular subregions and computes histograms of local features from each subregion, exhibits excellent results for natural scene classification. However, this approach is not applicable for brain tumors, because of the great variations in tumor shape and size. In this paper, we propose a method to enhance the classification performance. First, the augmented tumor region via image dilation is used as the ROI instead of the original tumor region because tumor surrounding tissues can also offer important clues for tumor types. Second, the augmented tumor region is split into increasingly fine ring-form subregions. We evaluate the efficacy of the proposed method on a large dataset with three feature extraction methods, namely, intensity histogram, gray level co-occurrence matrix (GLCM), and bag-of-words (BoW) model. Compared with using tumor region as ROI, using augmented tumor region as ROI improves the accuracies to 82.31% from 71.39%, 84.75% from 78.18%, and 88.19% from 83.54% for intensity histogram, GLCM, and BoW model, respectively. In addition to region augmentation, ring-form partition can further improve the accuracies up to 87.54%, 89.72%, and 91.28%. These experimental results demonstrate that the proposed method is feasible and effective for the classification of brain tumors in T1-weighted CE-MRI.http://europepmc.org/articles/PMC4598126?pdf=render |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Jun Cheng Wei Huang Shuangliang Cao Ru Yang Wei Yang Zhaoqiang Yun Zhijian Wang Qianjin Feng |
spellingShingle |
Jun Cheng Wei Huang Shuangliang Cao Ru Yang Wei Yang Zhaoqiang Yun Zhijian Wang Qianjin Feng Enhanced Performance of Brain Tumor Classification via Tumor Region Augmentation and Partition. PLoS ONE |
author_facet |
Jun Cheng Wei Huang Shuangliang Cao Ru Yang Wei Yang Zhaoqiang Yun Zhijian Wang Qianjin Feng |
author_sort |
Jun Cheng |
title |
Enhanced Performance of Brain Tumor Classification via Tumor Region Augmentation and Partition. |
title_short |
Enhanced Performance of Brain Tumor Classification via Tumor Region Augmentation and Partition. |
title_full |
Enhanced Performance of Brain Tumor Classification via Tumor Region Augmentation and Partition. |
title_fullStr |
Enhanced Performance of Brain Tumor Classification via Tumor Region Augmentation and Partition. |
title_full_unstemmed |
Enhanced Performance of Brain Tumor Classification via Tumor Region Augmentation and Partition. |
title_sort |
enhanced performance of brain tumor classification via tumor region augmentation and partition. |
publisher |
Public Library of Science (PLoS) |
series |
PLoS ONE |
issn |
1932-6203 |
publishDate |
2015-01-01 |
description |
Automatic classification of tissue types of region of interest (ROI) plays an important role in computer-aided diagnosis. In the current study, we focus on the classification of three types of brain tumors (i.e., meningioma, glioma, and pituitary tumor) in T1-weighted contrast-enhanced MRI (CE-MRI) images. Spatial pyramid matching (SPM), which splits the image into increasingly fine rectangular subregions and computes histograms of local features from each subregion, exhibits excellent results for natural scene classification. However, this approach is not applicable for brain tumors, because of the great variations in tumor shape and size. In this paper, we propose a method to enhance the classification performance. First, the augmented tumor region via image dilation is used as the ROI instead of the original tumor region because tumor surrounding tissues can also offer important clues for tumor types. Second, the augmented tumor region is split into increasingly fine ring-form subregions. We evaluate the efficacy of the proposed method on a large dataset with three feature extraction methods, namely, intensity histogram, gray level co-occurrence matrix (GLCM), and bag-of-words (BoW) model. Compared with using tumor region as ROI, using augmented tumor region as ROI improves the accuracies to 82.31% from 71.39%, 84.75% from 78.18%, and 88.19% from 83.54% for intensity histogram, GLCM, and BoW model, respectively. In addition to region augmentation, ring-form partition can further improve the accuracies up to 87.54%, 89.72%, and 91.28%. These experimental results demonstrate that the proposed method is feasible and effective for the classification of brain tumors in T1-weighted CE-MRI. |
url |
http://europepmc.org/articles/PMC4598126?pdf=render |
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