Brain Tumor MR Image Classification Using Convolutional Dictionary Learning With Local Constraint - Gu, Xiaoqing; Shen, Zongxuan; Xue, Jing; Fan, Yiqing; Ni, Tongguang (Frontiers in Neuroscience, 2021)
Brain tumor image classification is an important part of medical image processing. It assists doctors to make accurate diagnosis and treatment plans. Magnetic resonance (MR) imaging is one of the main imaging tools to study brain tissue. In this paper, we propose a brain tumor MR image classification method using convolutional dictionary learning with local constraint (CDLLC). Our method integrates the multi-layer dictionary learning into convolutional neural network (CNN) structure to explore the discriminative information. Encoding a vector on a dictionary can be considered as multiple projecting into new spaces, the obtained coding vector is sparse. Meanwhile, in order to preserve the geometric structure of data and utilizing the supervised information, we construct the local constraint of atoms through a supervised k-nearest neighbor graph, so that the discrimination of the obtained dictionary is strong. To solve the proposed problem, an efficient iterative optimization scheme is designed. In the experiment, two clinically relevant multiclass classification tasks on Cheng and REMBRANDT datasets were designed. The evaluation results demonstrate that our method is effective for brain tumor image classification, and it could outperform other comparisons.