Brain Lesion Segmentation Based on Joint Constraints of Low-Rank Representation and Sparse Representation
Joint Authors
Chen, Zhi
Ge, Ting
Mu, Ning
Zhan, Tianming
Gao, Wanrong
Mu, Shanxiang
Source
Computational Intelligence and Neuroscience
Issue
Vol. 2019, Issue 2019 (31 Dec. 2019), pp.1-11, 11 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2019-07-01
Country of Publication
Egypt
No. of Pages
11
Main Subjects
Abstract EN
The segmentation of brain lesions from a brain magnetic resonance (MR) image is of great significance for the clinical diagnosis and follow-up treatment.
An automatic segmentation method for brain lesions is proposed based on the low-rank representation (LRR) and the sparse representation (SR) theory.
The proposed method decomposes the brain image into the background part composed of brain tissue and the brain lesion part.
Considering that each pixel in the brain tissue can be represented by the background dictionary, a low-rank representation that incorporates sparsity-inducing regularization term is adopted to model the part.
Then, the linearized alternating direction method with adaptive penalty (LADMAP) was selected to solve the model, and the brain lesions can be obtained by the response of the residual matrix.
The presented model not only reflects the global structure of the image but also preserves the local information of the pixels, thus improving the representation accuracy.
The experimental results on the data of brain tumor patients and multiple sclerosis patients revealed that the proposed method is superior to several existing methods in terms of segmentation accuracy while realizing the segmentation automatically.
American Psychological Association (APA)
Ge, Ting& Mu, Ning& Zhan, Tianming& Chen, Zhi& Gao, Wanrong& Mu, Shanxiang. 2019. Brain Lesion Segmentation Based on Joint Constraints of Low-Rank Representation and Sparse Representation. Computational Intelligence and Neuroscience،Vol. 2019, no. 2019, pp.1-11.
https://search.emarefa.net/detail/BIM-1129679
Modern Language Association (MLA)
Ge, Ting…[et al.]. Brain Lesion Segmentation Based on Joint Constraints of Low-Rank Representation and Sparse Representation. Computational Intelligence and Neuroscience No. 2019 (2019), pp.1-11.
https://search.emarefa.net/detail/BIM-1129679
American Medical Association (AMA)
Ge, Ting& Mu, Ning& Zhan, Tianming& Chen, Zhi& Gao, Wanrong& Mu, Shanxiang. Brain Lesion Segmentation Based on Joint Constraints of Low-Rank Representation and Sparse Representation. Computational Intelligence and Neuroscience. 2019. Vol. 2019, no. 2019, pp.1-11.
https://search.emarefa.net/detail/BIM-1129679
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1129679