Meningioma is among the most common primary tumors of the brain. The firmness of Meningioma is a critical factor that influences operative strategy and patient counseling. Conventional methods to predict the tumor firmness rely on the correlation between the consistency of Meningioma and their preoperative MRI findings such as the signal intensity ratio between the tumor and the normal grey matter of the brain. Machine learning techniques have not been investigated yet to address the Meningioma firmness detection problem. The main purpose of this research is to couple supervised learning algorithms with typical descriptors for developing a computer-aided detection (CAD) of the Meningioma tumor firmness in MRI images. Specifically, Local Binary Patterns (LBP), Gray Level Co-occurrence Matrix (GLCM) and Discrete Wavelet Transform (DWT) are extracted from real labeled MRI-T2 weighted images and fed into classifiers, namely support vector machine (SVM) and k-nearest neighbor (KNN) algorithm to learn association between the visual properties of the region of interest and the pre-defined firm and soft classes. The learned model is then used to classify unlabeled MRI-T2 weighted images. This paper represents a baseline comparison of different features used in CAD system that intends to accurately recognize the Meningioma tumor firmness. The proposed system was implemented and assessed using a clinical dataset. Using LBP feature yielded the best performance with 95% of F-score, 87% of balanced accuracy and 0.87 of the area under ROC curve (AUC) when coupled with KNN classifier, respectively.

中文翻译：

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MRI图像中脑膜瘤肿瘤硬度的自动检测。

脑膜瘤是脑部最常见的原发性肿瘤之一。脑膜瘤的硬度是影响手术策略和患者咨询的关键因素。预测肿瘤硬度的传统方法依赖于脑膜瘤的一致性与其术前 MRI 发现之间的相关性，例如肿瘤与正常脑灰质之间的信号强度比。尚未研究机器学习技术来解决脑膜瘤硬度检测问题。本研究的主要目的是将监督学习算法与典型描述符相结合，以开发 MRI 图像中脑膜瘤肿瘤硬度的计算机辅助检测 (CAD)。具体来说，本地二进制模式（LBP），从真实标记的 MRI-T2 加权图像中提取灰度共生矩阵 (GLCM) 和离散小波变换 (DWT) 并输入分类器，即支持向量机 (SVM) 和 k-最近邻 (KNN) 算法以学习关联感兴趣区域的视觉属性与预定义的公司和软类之间。然后使用学习模型对未标记的 MRI-T2 加权图像进行分类。本文代表了 CAD 系统中使用的不同特征的基线比较，旨在准确识别脑膜瘤肿瘤的硬度。使用临床数据集实施和评估了所提议的系统。当与 KNN 分类器结合使用时，使用 LBP 特征分别产生了 95% 的 F-score、87% 的平衡精度和 0.87 的 ROC 曲线下面积 (AUC) 的最佳性能。