Brain Lesion Segmentation Based on Joint Constraints of Low-Rank Representation and Sparse Representation

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) theor...

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Veröffentlicht in:	Computational intelligence and neuroscience 2019-01, Vol.2019 (2019), p.1-11
Hauptverfasser:	Gao, Wanrong, Chen, Zhi, Zhan, Tianming, Mu, Ning, Ge, Ting, Mu, Shanxiang
Format:	Artikel
Sprache:	eng
Schlagworte:	Algorithms Brain Brain - diagnostic imaging Brain damage Brain diseases Brain Neoplasms - diagnostic imaging Brain research Brain tumors Humans Image Interpretation, Computer-Assisted - methods Image processing Image segmentation Information processing Lagrange multiplier Lesions Magnetic resonance Magnetic resonance imaging Magnetic Resonance Imaging - methods Medical imaging Medical imaging equipment Methods Multiple sclerosis Multiple Sclerosis - diagnostic imaging Neuroimaging Patients Pattern recognition Pattern Recognition, Automated - methods Pixels Principal components analysis Regularization Representations Signal processing
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