临床上原发性脑部淋巴瘤（PCNSL）和胶质母细胞瘤（GBM）的治疗方案存在很大差异，因此治疗前对二者的精确鉴别具有重要临床价值。本文提出一套基于稀疏表示体系的肿瘤自动鉴别方法，利用 PCNSL 和 GBM T1 加权磁共振成像（MRI）图像纹理细节信息的差异鉴别这两种肿瘤。首先，基于影像组学的思想，设计一种基于字典学习和稀疏表示的肿瘤纹理特征提取方法，将不同体积、不同形状的肿瘤区域转化为 968 维纹理特征；其次，针对提取特征存在的冗余问题，建立迭代稀疏表示方法选择少数高稳定性高分辨力的特征；最后，将选择的关键特征送入稀疏表示分类器（SRC）分类。利用十折法对数据集进行交叉验证，鉴别结果的准确率为 96.36%，敏感度为 96.30%，特异性为 96.43%。实验结果表明，本文方法不仅能够有效地鉴别 PCNSL 和 GBM，还避免了使用先进 MRI 鉴别肿瘤时存在的参数提取问题，在实际应用中具有较强的鲁棒性。
It is of great clinical significance in the differential diagnosis of primary central nervous system lymphoma (PCNSL) and glioblastoma (GBM) because there are enormous differences between them in terms of therapeutic regimens. In this paper, we propose a system based on sparse representation for automatic classification of PCNSL and GBM. The proposed system distinguishes the two tumors by using of the different texture detail information of the two tumors on T1 contrast magnetic resonance imaging (MRI) images. First, inspired by the process of radiomics, we designed a dictionary learning and sparse representation-based method to extract texture information, and with this approach, the tumors with different volume and shape were transformed into 968 quantitative texture features. Next, aiming at the problem of the redundancy in the extracted features, feature selection based on iterative sparse representation was set up to select some key texture features with high stability and discrimination. Finally, the selected key features are used for differentiation based on sparse representation classification (SRC) method. By using ten-fold cross-validation method, the differentiation based on the proposed approach presents accuracy of 96.36%, sensitivity 96.30%, and specificity 96.43%. Experimental results show that our approach not only effectively distinguish the two tumors but also has strong robustness in practical application since it avoids the process of parameter extraction on advanced MRI images.
引用本文： 吴国庆, 李泽榉, 汪源源, 余锦华, 陈银生, 陈忠平. 基于稀疏表示体系的原发性脑部淋巴瘤和胶质母细胞瘤图像鉴别. 生物医学工程学杂志, 2018, 35(5): 754-760. doi: 10.7507/1001-5515.201705061 复制