Abstract The manuscript authenticates the effectiveness of fusing texture and geometrical (GEO) features in magnetic resonance imaging (MRI) for tumor classification. The presented technique is evaluated on two MRI including T2 and FLAIR. The tumor region is enhanced using fast non-local mean (FNLM) method with 4 × 4 patch size. Otsu algorithm is used for tumor segmentation. Moreover, multiple features are extracted for example local binary pattern (LBP), histogram of oriented gradients (HOG) and GEO (area, circularity, filled area, and perimeter) across each segmented image. These acquired features are merged into a single dimensional vector for prediction. In the end, the fused vector is used with multiple classifiers which proved that features fusion provides good results as compared with individual features.

中文翻译：

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使用特征融合的基于统一补丁的脑肿瘤检测方法

摘要 该手稿验证了融合纹理和几何 (GEO) 特征在磁共振成像 (MRI) 中对肿瘤分类的有效性。所提出的技术在包括 T2 和 FLAIR 在内的两个 MRI 上进行了评估。使用具有 4 × 4 块大小的快速非局部均值 (FNLM) 方法增强肿瘤区域。Otsu算法用于肿瘤分割。此外，在每个分割图像上提取多个特征，例如局部二值模式 (LBP)、定向梯度直方图 (HOG) 和 GEO（面积、圆度、填充区域和周长）。这些获得的特征被合并成一个单维向量以进行预测。最后，融合向量与多个分类器一起使用，证明与单个特征相比，特征融合提供了良好的结果。