[Grading method of inhomogeneity of contrast-enhanced ultrasound for rectal tumors based on gray level co-occurrence matrix].

Transrectal contrast-enhanced ultrasound (CEUS) is an important examination for rectal tumors. The inhomogeneity of the CEUS images has important clinical significance. However, there is no objective method to evaluate this index. In this study, a method based on gray-level co-occurrence matrix (GLCM) is proposed to extract texture features of images and grade these images according the inhomogeneity. Specific processes include compressing the gray level of the image, calculating the texture statistics of gray level co-occurrence matrix, combining feature selection and principal component analysis (PCA) for dimensionality reduction, and training and validating quadratic discriminant analysis (QDA). After ten cross-validation, the overall accuracy rate of machine classification was 87.01%, and the accuracy of each level was as follows: Grade Ⅰ 52.94%, Grade Ⅱ 96.48% and Grade Ⅲ 92.35% respectively. The proposed method has high accuracy in judging grade Ⅱ and Ⅲ images, which can help to identify the grade of inhomogeneity of contrast-enhanced ultrasound images of rectal tumors, and may be used to assist clinical doctors in judging the grade of inhomogeneity of contrast-enhanced ultrasound of rectal tumors. 经直肠超声造影是直肠肿瘤常规检查方法，不同肿瘤内的造影剂分布的不均匀程度是重要的影像特征，依赖人工方法可以对该特征进行分级。但是针对大量数据时，人工分级繁琐缓慢，且结果容易受到影响。本文提出了一种基于灰度共生矩阵（GLCM）的提取直肠肿瘤超声造影图像内造影剂分布特征的计算机分级方法。具体流程包括压缩图片的灰度、计算灰度共生矩阵的纹理统计量、结合特征选择和主成分分析（PCA）进行降维以及训练和验证二次判别分析模型（QDA）。经过十次交叉验证，机器分级的总体准确率为 87.01%；各级的准确率分别为：Ⅰ级 52.94%；Ⅱ级 96.48%；Ⅲ级 92.35%。本文方法对Ⅱ级及Ⅲ级图像的判定准确率较高，可以帮助识别直肠肿瘤超声造影图像内的造影剂分布特征，有望用于辅助判定直肠肿瘤超声造影的不均匀程度。.