Global Biomedical Technologies, Inc, 208 Otter Glen CT, Roseville, CA 95661 USA.
J Ultrasound Med. 2014 Feb;33(2):245-53. doi: 10.7863/ultra.33.2.245.
Computer-aided diagnostic (CAD) techniques aid physicians in better diagnosis of diseases by extracting objective and accurate diagnostic information from medical data. Hashimoto thyroiditis is the most common type of inflammation of the thyroid gland. The inflammation changes the structure of the thyroid tissue, and these changes are reflected as echogenic changes on ultrasound images. In this work, we propose a novel CAD system (a class of systems called ThyroScan) that extracts textural features from a thyroid sonogram and uses them to aid in the detection of Hashimoto thyroiditis.
In this paradigm, we extracted grayscale features based on stationary wavelet transform from 232 normal and 294 Hashimoto thyroiditis-affected thyroid ultrasound images obtained from a Polish population. Significant features were selected using a Student t test. The resulting feature vectors were used to build and evaluate the following 4 classifiers using a 10-fold stratified cross-validation technique: support vector machine, decision tree, fuzzy classifier, and K-nearest neighbor.
Using 7 significant features that characterized the textural changes in the images, the fuzzy classifier had the highest classification accuracy of 84.6%, sensitivity of 82.8%, specificity of 87.0%, and a positive predictive value of 88.9%.
The proposed ThyroScan CAD system uses novel features to noninvasively detect the presence of Hashimoto thyroiditis on ultrasound images. Compared to manual interpretations of ultrasound images, the CAD system offers a more objective interpretation of the nature of the thyroid. The preliminary results presented in this work indicate the possibility of using such a CAD system in a clinical setting after evaluating it with larger databases in multicenter clinical trials.
计算机辅助诊断 (CAD) 技术通过从医学数据中提取客观准确的诊断信息，帮助医生更好地诊断疾病。桥本甲状腺炎是甲状腺最常见的炎症类型。炎症改变甲状腺组织的结构，这些变化在超声图像上反映为回声变化。在这项工作中，我们提出了一种新的 CAD 系统（一类称为 ThyroScan 的系统），该系统从甲状腺超声图像中提取纹理特征，并使用这些特征辅助桥本甲状腺炎的检测。
在这种范例中，我们从波兰人群中获得的 232 个正常和 294 个桥本甲状腺炎受累的甲状腺超声图像中，基于平稳小波变换提取灰度特征。使用学生 t 检验选择显著特征。所得特征向量用于构建和评估以下 4 种分类器：支持向量机、决策树、模糊分类器和 K-最近邻。
使用 7 个特征来描述图像中的纹理变化，模糊分类器的分类准确率最高，为 84.6%，灵敏度为 82.8%，特异性为 87.0%，阳性预测值为 88.9%。
提出的 ThyroScan CAD 系统使用新的特征来非侵入性地检测超声图像中桥本甲状腺炎的存在。与超声图像的手动解释相比，CAD 系统提供了对甲状腺性质更客观的解释。本工作中提出的初步结果表明，在多中心临床试验中使用更大的数据库对其进行评估后，该 CAD 系统有可能在临床环境中使用。
