School of Electronic and Optical Engineering, Nanjing University of Science and Technology, No. 200, Xiaolingwei, Xuanwu District, Nanjing, Jiangsu 210094, China.
School of Science, Jinling Institute of Technology, No. 99, Hongjing Avenue, Jiangning District, Nanjing, Jiangsu 211169, China.
Comput Intell Neurosci. 2019 Jul 1;2019:9378014. doi: 10.1155/2019/9378014. eCollection 2019.
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.
脑磁共振（MR）图像中脑病变的分割对于临床诊断和随访治疗具有重要意义。提出了一种基于低秩表示（LRR）和稀疏表示（SR）理论的脑病变自动分割方法。该方法将脑图像分解为由脑组织组成的背景部分和脑病变部分。考虑到脑组织中的每个像素都可以由背景字典表示，因此采用了一种结合稀疏诱导正则化项的低秩表示来对该部分进行建模。然后，选择具有自适应惩罚项的线性交替方向法（LADMAP）来求解该模型，并通过残差矩阵的响应获得脑病变。所提出的模型不仅反映了图像的全局结构，而且保留了像素的局部信息，从而提高了表示的准确性。在脑肿瘤患者和多发性硬化症患者的数据上进行的实验结果表明，与几种现有的方法相比，该方法在实现自动分割的同时，在分割准确性方面具有优势。
