CLC number: TP181; R739.41
On-line Access: 2024-08-27
Received: 2023-10-17
Revision Accepted: 2024-05-08
Crosschecked: 2018-04-14
Cited: 0
Clicked: 6756
Ji-jun Tong, Peng Zhang, Yu-xiang Weng, Dan-hua Zhu. Kernel sparse representation for MRI image analysis in automatic brain tumor segmentation[J]. Frontiers of Information Technology & Electronic Engineering, 2018, 19(4): 471-480.
@article{title="Kernel sparse representation for MRI image analysis in automatic brain tumor segmentation",
author="Ji-jun Tong, Peng Zhang, Yu-xiang Weng, Dan-hua Zhu",
journal="Frontiers of Information Technology & Electronic Engineering",
volume="19",
number="4",
pages="471-480",
year="2018",
publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.1620342"
}
%0 Journal Article
%T Kernel sparse representation for MRI image analysis in automatic brain tumor segmentation
%A Ji-jun Tong
%A Peng Zhang
%A Yu-xiang Weng
%A Dan-hua Zhu
%J Frontiers of Information Technology & Electronic Engineering
%V 19
%N 4
%P 471-480
%@ 2095-9184
%D 2018
%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.1620342
TY - JOUR
T1 - Kernel sparse representation for MRI image analysis in automatic brain tumor segmentation
A1 - Ji-jun Tong
A1 - Peng Zhang
A1 - Yu-xiang Weng
A1 - Dan-hua Zhu
J0 - Frontiers of Information Technology & Electronic Engineering
VL - 19
IS - 4
SP - 471
EP - 480
%@ 2095-9184
Y1 - 2018
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/FITEE.1620342
Abstract: The segmentation of brain tumor plays an important role in diagnosis, treatment planning, and surgical simulation. The precise segmentation of brain tumor can help clinicians obtain its location, size, and shape information. We propose a fully automatic brain tumor segmentation method based on kernel sparse coding. It is validated with 3D multiple-modality magnetic resonance imaging (MRI). In this method, MRI images are pre-processed first to reduce the noise, and then kernel dictionary learning is used to extract the nonlinear features to construct five adaptive dictionaries for healthy tissues, necrosis, edema, non-enhancing tumor, and enhancing tumor tissues. sparse coding is performed on the feature vectors extracted from the original MRI images, which are a patch of m×m×m around the voxel. A kernel-clustering algorithm based on dictionary learning is developed to code the voxels. In the end, morphological filtering is used to fill in the area among multiple connected components to improve the segmentation quality. To assess the segmentation performance, the segmentation results are uploaded to the online evaluation system where the evaluation metrics dice score, positive predictive value (PPV), sensitivity, and kappa are used. The results demonstrate that the proposed method has good performance on the complete tumor region (dice: 0.83; PPV: 0.84; sensitivity: 0.82), while slightly worse performance on the tumor core (dice: 0.69; PPV: 0.76; sensitivity: 0.80) and enhancing tumor (dice: 0.58; PPV: 0.60; sensitivity: 0.65). It is competitive to the other groups in the brain tumor segmentation challenge. Therefore, it is a potential method in differentiation of healthy and pathological tissues.
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