School of Science, Jinling Institute of Technology, Nanjing 211169, China.
School of Electronic and Optical Engineering, Nanjing University of Science and Technology, Nanjing 210094, China.
Comput Intell Neurosci. 2022 Jun 24;2022:3514988. doi: 10.1155/2022/3514988. eCollection 2022.
Given the need for quantitative measurement and 3D visualisation of brain tumours, more and more attention has been paid to the automatic segmentation of tumour regions from brain tumour magnetic resonance (MR) images. In view of the uneven grey distribution of MR images and the fuzzy boundaries of brain tumours, a representation model based on the joint constraints of kernel low-rank and sparsity (KLRR-SR) is proposed to mine the characteristics and structural prior knowledge of brain tumour image in the spectral kernel space. In addition, the optimal kernel based on superpixel uniform regions and multikernel learning (MKL) is constructed to improve the accuracy of the pairwise similarity measurement of pixels in the kernel space. By introducing the optimal kernel into KLRR-SR, the coefficient matrix can be solved, which allows brain tumour segmentation results to conform with the spatial information of the image. The experimental results demonstrate that the segmentation accuracy of the proposed method is superior to several existing methods under different indicators and that the sparsity constraint for the coefficient matrix in the kernel space, which is integrated into the kernel low-rank model, has certain effects in preserving the local structure and details of brain tumours.
鉴于对脑肿瘤进行定量测量和三维可视化的需求，越来越多的人关注从脑肿瘤磁共振（MR）图像中自动分割肿瘤区域。针对 MR 图像灰度分布不均匀和脑肿瘤边界模糊的问题，提出了一种基于核低秩稀疏（KLRR-SR）联合约束的表示模型，以挖掘脑肿瘤图像在谱核空间中的特征和结构先验知识。此外，构建了基于超像素均匀区域和多核学习（MKL）的最优核，以提高核空间中像素对相似性度量的准确性。通过引入最优核到 KLRR-SR 中，可以求解系数矩阵，从而使脑肿瘤分割结果符合图像的空间信息。实验结果表明，在所提出的方法下，不同指标的分割准确性均优于几种现有方法，并且核空间中系数矩阵的稀疏性约束，被集成到核低秩模型中，对于保持脑肿瘤的局部结构和细节具有一定的效果。
