A Glioma Detection and Segmentation Method in MR Imaging

Hao CHEN; Guang LI; Yang LIU; Yongqian QIANG

doi:10.11999/JEIT200033

Volume 43 Issue 4

Apr. 2021

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Article Navigation > Journal of Electronics & Information Technology > 2021 > 43(4): 992-1002

Hao CHEN, Guang LI, Yang LIU, Yongqian QIANG. A Glioma Detection and Segmentation Method in MR Imaging[J]. Journal of Electronics & Information Technology, 2021, 43(4): 992-1002. doi: 10.11999/JEIT200033

Citation:

Hao CHEN, Guang LI, Yang LIU, Yongqian QIANG. A Glioma Detection and Segmentation Method in MR Imaging[J]. Journal of Electronics & Information Technology, 2021, 43(4): 992-1002. doi: 10.11999/JEIT200033

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A Glioma Detection and Segmentation Method in MR Imaging

doi: 10.11999/JEIT200033

Hao CHEN^{1, 2
,
,},
Guang LI¹,
Yang LIU¹,
Yongqian QIANG³

1.
School of Computer, Xi’an University of Posts & Telecommunications, Xi’an 710121, China
2.
Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing, Xi’an University of Post and Telecommunications, Xi’an 710121, China
3.
First Affiliated Hospital of Xi’an Jiaotong University, Xi’an 710061, China

Funds: The National Natural Science Foundation of China (61876138, 61203311), The Natural Science Basic Research Program of Shaanxi Province (2019JM-365), The Scientific Research Program Funded by Shaanxi Provincial Education Department of China (17JK0701), The Graduate Innovation Foundation of Xi’an University of Posts & Telecommunications (CXJJ2017036)

Received Date: 2020-01-09
Rev Recd Date: 2020-06-15

Available Online: 2020-07-22

Publish Date: 2021-04-20

Abstract

Abstract

The glioma detection and focus segmentation in Magnetic Resonance Imaging (MRI) has important value for the therapeutic schedule selection and the surgical operations. In order to improve the detection efficiency and segmentation accuracy for glioma, this paper proposes a two-stage calculating method. First, a light convolutional neural network is designed to implement rapidly detection and localization for the glioma in MR images. Then, the peritumoral edema, non-enhancing tumor, enhancing tumor, and normal are classified and segmented from each other through an Ensemble Learning (EL) process. In order to improve the accuracy of segmentation, 416 radiomics features extracted from multi-modal MR images and 128 CNN features extracted by a convolutional neural network are mixed. The feature vector consisting of 298 features for classification learning are formed after a feature reduction process. In order to verify the performance of the proposed algorithm, experiments are carried out on the BraTS2017 dataset. The experimental results show that the proposed method can quickly detect and locate the tumor. The overall segmentation accuracy is improved distinctly with respect to 4 state-of-the-art approaches.
- Tumor detection,
- Focus segmentation,
- Features selection,
- Ensemble Learning (EL)

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References(25)

References

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