Significant successes in machine learning approaches to image analysis for various applications have energized strong interest in automated diagnostic support systems for medical images. The evolving in-depth understanding of the way carcinogenesis changes the texture of cellular networks of a mass/tumor has been informing such diagnostics systems with use of more suitable image texture features and their extraction methods. Several texture features have been recently applied in discriminating malignant and benign ovarian masses by analysing B-mode images from ultrasound scan of the ovary with different levels of performance. However, comparative performance evaluation of these reported features using common sets of clinically approved images is lacking. This paper presents an empirical evaluation of seven commonly used texture features (histograms, moments of histogram, local binary patterns [256-bin and 59-bin], histograms of oriented gradients, fractal dimensions, and Gabor filter), using a collection of 242 ultrasound scan images of ovarian masses of various pathological characteristics. The evaluation examines not only the effectiveness of classification schemes based on the individual texture features but also the effectiveness of various combinations of these schemes using the simple majority-rule decision level fusion. Trained support vector machine classifiers on the individual texture features without any specific pre-processing, achieve levels of accuracy between 75% and 85% where the seven moments and the 256-bin LBP are at the lower end while the Gabor filter is at the upper end. Combining the classification results of the top k (k = 3, 5, 7) best performing features further improve the overall accuracy to a level between 86% and 90%. These evaluation results demonstrate that each of the investigated image-based texture features provides informative support in distinguishing benign or malignant ovarian masses.

机器学习方法对各种应用的图像分析取得了巨大成功，激发了人们对医学图像自动诊断支持系统的浓厚兴趣。对致癌作用改变肿块/肿瘤细胞网络结构的方式的深入理解已经为此类诊断系统提供了信息，使用更合适的图像纹理特征及其提取方法。通过分析来自具有不同性能水平的卵巢超声扫描的 B 模式图像，最近已将几种纹理特征应用于区分恶性和良性卵巢肿块。然而，缺乏使用临床批准的常用图像集对这些报告特征的比较性能评估。本文使用 242各种病理特征的卵巢肿块的超声扫描图像。评估不仅检查基于单个纹理特征的分类方案的有效性，还检查使用简单多数规则决策级融合的这些方案的各种组合的有效性。在单个纹理特征上训练支持向量机分类器，无需任何特定的预处理，达到 75% 和 85% 之间的准确度水平，其中七个矩和 256-bin LBP 位于下端，而 Gabor 过滤器位于上端结尾。k ( k  = 3, 5, 7) 性能最佳的特征进一步将整体准确度提高到 86% 到 90% 之间的水平。这些评估结果表明，每个研究的基于图像的纹理特征都为区分良性或恶性卵巢肿块提供了信息支持。