Department of Computer Biomedical Systems, University of Silesia, Institute of Computer Science, Sosnowiec, Poland.
Biomed Eng Online. 2012 Nov 28;11:91. doi: 10.1186/1475-925X-11-91.
This paper shows the influence of a measurement method of features in the diagnosis of Hashimoto's disease. Sensitivity of the algorithm to changes in the parameters of the ROI, namely shift, resizing and rotation, has been presented. The obtained results were also compared to the methods known from the literature in which decision trees or average gray level thresholding are used.
In the study, 288 images obtained from patients with Hashimoto's disease and 236 images from healthy subjects have been analyzed. For each person, an ultrasound examination of the left and right thyroid lobe in transverse and longitudinal sections has been performed.
With the use of the developed algorithm, a discriminant analysis has been conducted for the following five options: linear, diaglinear, quadratic, diagquadratic and mahalanobis. The left and right thyroid lobes have been analyzed both together and separately in transverse and longitudinal sections. In addition, the algorithm enabled to analyze specificity and sensitivity as well as the impact of sensitivity of ROI shift, repositioning and rotation on the measured features.
The analysis has shown that the highest accuracy was obtained for the longitudinal section (LD) with the method of linear, yielding sensitivity = 76%, specificity = 95% and accuracy ACC = 84%. The conducted sensitivity assessment confirms that changes in the position and size of the ROI have little effect on sensitivity and specificity. The analysis of all cases, that is, images of the left and right thyroid lobes in transverse and longitudinal sections, has shown specificity ranging from 60% to 95% and sensitivity from 62% to 89%. Additionally, it was shown that the value of ACC for the method using decision trees as a classifier is equal to 84% for the analyzed data. Thresholding of average brightness of the ROI gave ACC equal to 76%.
本文展示了一种在桥本氏病诊断中特征测量方法的影响。 算法对 ROI 位置、大小和旋转参数变化的敏感性已经呈现。 还将获得的结果与文献中已知的方法进行了比较，其中使用决策树或平均灰度阈值法。
在这项研究中，分析了 288 张来自桥本氏病患者和 236 张来自健康受试者的图像。 对每个人进行了左、右甲状腺叶的横向和纵向超声检查。
使用开发的算法，对以下五个选项进行了判别分析：线性、对角线性、二次、对角二次和马哈拉诺比斯。 分别对横向和纵向切片中的左、右甲状腺叶进行了分析。 此外，该算法还可以分析特异性和敏感性以及 ROI 位移、重新定位和旋转对测量特征的敏感性的影响。
分析表明，在 LD 中，线性方法的准确率最高，敏感性为 76%，特异性为 95%，准确性 ACC 为 84%。 进行的敏感性评估证实，ROI 位置和大小的变化对敏感性和特异性的影响很小。 对所有病例的分析，即横向和纵向甲状腺叶的图像，特异性范围为 60%至 95%，敏感性为 62%至 89%。 此外，还表明，对于使用决策树作为分类器的方法，分析数据的 ACC 值等于 84%。 ROI 平均亮度阈值的 ACC 值等于 76%。
