To perform computer-aided diagnosis of the thyroid nodules on ultrasound images, the location and boundary of nodules should be clearly defined. However, the identification of thyroid nodule boundary is a difficult issue due to the biological characteristics of the nodules, the physics and quality of ultrasound imaging, and the subjective factors and operating conditions of the operator. In this study, we propose a novel and semiautomatic method for detecting the boundary of thyroid nodule based on the Variance-Reduction (V-R) statistics without image preprocessing. The region of interest (ROI) is first automatically generated according to the initial inputs of the nodule’s major and minor axes. The boundary candidate pixel points are then extracted by using the V-R statistics from the grayscale values of all pixel points in the ROI. Three filtering methods are further applied to eliminate the outlier pixel points to ensure that the remaining candidate pixel points are located on the nodule boundary. Finally, the remaining pixel points are smoothened and linked together to form the final boundary. The proposed method is validated with ultrasound images of 538 thyroid nodules, with manual delineation by experienced radiologist as gold standard. The effectiveness is evaluated and compared with previous publications using boundary error metrics and overlapping area metrics with the same data set. The results show that the normalized average mean boundary error is 1.02%, the true positive overlapping area ratio achieves 93.66% and false positive overlapping area ratio is limited to 7.68%. In conclusion, our proposed method is reliable and effective in detecting thyroid nodule boundary on ultrasound images.

中文翻译：

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一种在超声图像上检测甲状腺结节边界的方差减少方法

要在超声图像上对甲状腺结节进行计算机辅助诊断，应明确定义结节的位置和边界。然而，由于结节的生物学特性、超声成像的物理和质量以及操作者的主观因素和操作条件，甲状腺结节边界的识别是一个难题。在这项研究中，我们提出了一种基于方差减少 (VR) 统计数据而无需图像预处理的新型半自动检测甲状腺结节边界的方法。感兴趣区域 (ROI) 首先根据结节长轴和短轴的初始输入自动生成。然后使用 VR 统计从 ROI 中所有像素点的灰度值中提取边界候选像素点。进一步应用三种过滤方法来消除离群像素点，以确保剩余的候选像素点位于结节边界上。最后，剩余的像素点被平滑并链接在一起以形成最终边界。所提出的方法通过 538 个甲状腺结节的超声图像进行了验证，并由经验丰富的放射科医生手动勾画作为金标准。使用相同数据集的边界误差度量和重叠区域度量来评估有效性并与以前的出版物进行比较。结果表明，归一化平均平均边界误差为1.02%，真阳性重叠面积比达到93.66%，假阳性重叠面积比限制在7.68%。综上所述，