Quantitative glioma grading using transformed gray-scale invariant textures of MRI
Quantitative glioma grading using transformed gray-scale invariant textures of MRI
复制标题
DOI：
10.1016/j.compbiomed.2017.02.012
复制
发表时间：
2017-04-01
影响因子：
7.7
通讯作者：
Lo, Chung-Ming
中科院分区：
文献类型：
--
作者：
Hsieh, Kevin Li-Chun;Chen, Cheng-Yu;Lo, Chung-Ming
背景：提出了一种基于强度不变磁共振 (MR) 成像特征的计算机辅助诊断 (CAD) 系统，用于对神经胶质瘤进行分级，以普遍应用于各种扫描系统和设置。方法：总共有 34 例胶质母细胞瘤和 73 例低级别神经胶质瘤构成图像数据库，以评估所提出的 CAD 系统。对于每种情况，MR 图像上的局部纹理都被转换为强度不变的局部二值模式（LBP）。从 LBP 中提取定量图像特征，包括直方图矩和纹理，并将其组合到逻辑回归分类器中，以建立恶性肿瘤预测模型。结果：基于LBP特征的CAD系统的性能达到了93％（100/107）的准确率，97％（33/34）的灵敏度，99％（67/68）的阴性预测值，以及0.94的接受者操作特征曲线下面积（Az），明显优于传统纹理特征：准确率84％ (90/107)，敏感性为 76% (26/34)，阴性预测值为 89% (64/72)，Az 为 0.89，p 值分别为 0.0303、0.0122、0.0201 和 0.0334。 结论：从 MR 图像中提取了更稳健的纹理特征，并将其组合到一个明显更好的 CAD 系统中进行区分来自低级别胶质瘤的胶质母细胞瘤。所提出的 CAD 系统在各种成像系统和设置的临床使用中将更加实用。
Background: A computer-aided diagnosis (CAD) system based on intensity-invariant magnetic resonance (MR) imaging features was proposed to grade gliomas for general application to various scanning systems and settings.Method: In total, 34 glioblastomas and 73 lower-grade gliomas comprised the image database to evaluate the proposed CAD system. For each case, the local texture on MR images was transformed into a local binary pattern (LBP) which was intensity-invariant. From the LBP, quantitative image features, including the histogram moment and textures, were extracted and combined in a logistic regression classifier to establish a malignancy prediction model. The performance was compared to conventional texture features to demonstrate the improvement.Results: The performance of the CAD system based on LBP features achieved an accuracy of 93% (100/107), a sensitivity of 97% (33/34), a negative predictive value of 99% (67/68), and an area under the receiver operating characteristic curve (Az) of 0.94, which were significantly better than the conventional texture features: an accuracy of 84% (90/107), a sensitivity of 76% (26/34), a negative predictive value of 89% (64/72), and an Az of 0.89 with respective p values of 0.0303, 0.0122, 0.0201, and 0.0334.Conclusions: More-robust texture features were extracted from MR images and combined into a significantly better CAD system for distinguishing glioblastomas from lower-grade gliomas. The proposed CAD system would be more practical in clinical use with various imaging systems and settings.