Data_Sheet_1_A survey of testicular texture in canine ultrasound images.zip

IntroductionComputer-based texture analysis provides objective data that can be extracted from medical images, including ultrasound images. One popular methodology involves the generation of a gray-level co-occurrence matrix (GLCM) from the image, and from that matrix, texture fractures can be extracted.
MethodsWe performed texture analysis on 280 ultrasound testicular images obtained from 70 dogs and explored the resulting texture data, by means of principal component analysis (PCA).
ResultsVarious abnormal lesions were identified subjectively in 35 of the 280 cropped images. In 16 images, pinpoint-to-small, well-defined, hyperechoic foci were identified without acoustic shadowing. These latter images were classified as having “microliths.” The remaining 19 images with other lesions and areas of non-homogeneous testicular parenchyma were classified as “other.” In the PCA scores plot, most of the images with lesions were clustered. These clustered images represented by those scores had higher values for the texture features entropy, dissimilarity, and contrast, and lower values for the angular second moment and energy in the first principal component. Other data relating to the dogs, including age and history of treatment for prostatomegaly or chemical castration, did not show clustering on the PCA.
DiscussionThis study illustrates that objective texture analysis in testicular ultrasound correlates to some of the visual features used in subjective interpretation and provides quantitative data for parameters that are highly subjective by human observer analysis. The study demonstrated a potential for texture analysis in prediction models in dogs with testicular abnormalities.

引言
基于计算机的纹理分析可从包括超声图像在内的医学影像中提取客观数据。当前主流的分析方法之一是从图像中生成灰度共生矩阵（gray-level co-occurrence matrix, GLCM），并基于该矩阵提取纹理特征。

方法
本研究对70只犬的280张睾丸超声图像开展纹理分析，并借助主成分分析（principal component analysis, PCA）对得到的纹理数据进行探索。

结果
研究人员在280张裁剪后的图像中主观识别出35张存在异常病变。其中16张图像可见针尖至小型、边界清晰的高回声灶，且无声影，此类图像被归类为“微石症”。剩余19张存在其他病变及睾丸实质不均匀区域的图像被归类为“其他”。在主成分分析得分图中，多数病变图像呈现出聚集趋势。在第一主成分维度上，这些聚集的图像对应的纹理特征参数熵、相异性和对比度数值更高，而角二阶矩与能量数值更低。与犬只相关的其他数据，包括年龄、前列腺肥大治疗史或化学去势史，在主成分分析中未呈现聚集特征。

讨论
本研究表明，睾丸超声的客观纹理分析与主观判读所采用的部分视觉特征具有相关性，同时可为人类观察者分析中主观性极强的参数提供定量数据。本研究证实，纹理分析在犬睾丸异常的预测模型中具备应用潜力。