Abstract
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.
Highlights
In recent years, brain diseases have become one of the most important diseases that endanger the health of human beings. e segmentation of brain lesions from brain images can be a valuable reference for the follow-up treatment of patients [1]
The above methods can segment the lesion regions to some extent, it is necessary to know the prior information of the lesions in advance. Erefore, they are only applicable to detect certain brain diseases. e purpose of the batch detection for brain images and the automatic segmentation of brain lesions cannot be achieved. Because of these factors and concerns, we propose a novel automatic segmentation approach for brain lesions based on the joint constraints of low-rank representation (LRR) and sparse representation (SR) (JCLRRSR) in this paper
Since the LRR model is able to describe the whole structure of the brain tissues in image, while the SR model is good at characterizing the local information of the pixels, the proposed method can improve the representation accuracy of the image, thereby increasing the segmentation accuracy of the brain lesions
Summary
Brain diseases have become one of the most important diseases that endanger the health of human beings. e segmentation of brain lesions from brain images can be a valuable reference for the follow-up treatment of patients [1]. It can be seen from these images that each sequence has a different effect on the display of the brain lesion regions. E purpose of the batch detection for brain images and the automatic segmentation of brain lesions cannot be achieved Because of these factors and concerns, we propose a novel automatic segmentation approach for brain lesions based on the joint constraints of LRR and SR (JCLRRSR) in this paper. Since the LRR model is able to describe the whole structure of the brain tissues in image, while the SR model is good at characterizing the local information of the pixels, the proposed method can improve the representation accuracy of the image, thereby increasing the segmentation accuracy of the brain lesions.
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