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MRI-based radiomics to predict lipomatous soft tissue tumors malignancy: a pilot study

机译:基于MRI的射线瘤预测脂肪瘤软组织肿瘤恶性肿瘤:试验研究

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摘要

Radiome extraction pipeline. Size and shape features were extracted from the binary mask. Intensity distribution features were extracted from masked MR images from the histogram built with 256 bins. Image gray levels were discretized in a smaller number of gray levels with an equal probability algorithm. Images were discretized in 8, 16, 24, 32, 40, 48, and 64 Gy levels. For each discretization level, four matrices were built: GLCM (Gray-level co-occurrence matrix), GLRLM (Gray-level run length matrix), GLSZM (Gray-level size zone matrix), and NGTDM (Neighborhood gray tone difference matrix) from which characteristics were extracted, then averaged. Frequency domain-based texture features were extracted using a Gabor filtering
机译:放射线提取管道。从二进制掩模提取尺寸和形状特征。从用256个箱构建的直方图中从掩模MR图像中提取强度分布特征。图像灰度水平以较少数量的灰度级分离,具有相等的概率算法。在8,16,24,32,40,48和64 Gy水平中离散地分散图像。对于每个离散化级别,构建了四个矩阵:GLCM(灰度级共发生矩阵),GLRLM(灰级运行长度矩阵),GLSZM(灰级大小阵列矩阵)和NGTDM(邻域灰色音差矩阵)提取的特征从哪个特征进行平均。使用Gabor滤波提取基于频域的纹理特征

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