Accurate delineation of thyroid nodules in ultrasound images is vital for computer-aided diagnosis. Most segmentation methods are semi-automated for thyroid nodules and require manual intervention, which increases the processing time and errors. We propose an automated intuitionistic fuzzy active contour method (IFACM) that integrates intuitionistic fuzzy clustering with an active contour for thyroid nodule segmentation using ultrasound images. Intuitionistic fuzzy clustering is used for the initialization of an active contour and estimation of the parameters required to automatically control the curve evolution. The IFACM was tested extensively on both artificial and real ultrasound images. The IFACM obtained a higher value of true positive (95.1% ± 2.86%), overlap metric (93.1 ± 2.95%), and dice coefficient (90.90 ± 3.08$),$indicating that the boundary delineated by the IFACM fits best to true nodules. Moreover, it obtained a lower value of false positive (04.1% ± 3.24%) and Hausdorff distance (0.50 ± 0.21 in pixels), further verifying the higher similarity of shape and boundary, respectively. According to the significance test, the results of the proposed method were more significant than those of the other segmentation methods. The main benefit of the IFACM is the automatic identification of nodules on the basis of image characteristics, which eliminates manual intervention. In all the experiments, all initial contours were automatically defined closer to the boundaries of the nodule, which is a benefit of the IFACM. Moreover, this method can segment multiple nodules in a single image efficiently.

超声图像中甲状腺结节的准确描绘对于计算机辅助诊断至关重要。大多数分割方法对于甲状​​腺结节是半自动化的，需要人工干预，这增加了处理时间和错误。我们提出了一种自动直觉模糊主动轮廓方法（IFACM），该方法将直觉模糊聚类与主动轮廓集成在一起，以使用超声图像对甲状腺结节进行分割。直觉模糊聚类用于活动轮廓的初始化和自动控制曲线演变所需参数的估计。在人造和真实超声图像上对IFACM进行了广泛的测试。IFACM获得较高的真阳性值（95.1％±2.86％），重叠度量（93.1±2.95％）和骰子系数（90.90±3.08）$），$表示由IFACM划定的边界最适合真实结节。此外，它获得了较低的假阳性值（04.1％±3.24％）和Hausdorff距离（像素中的0.50±0.21），从而进一步验证了形状和边界的较高相似性。根据显着性检验，提出的方法的结果比其他分割方法的结果更有意义。IFACM的主要优点是根据图像特征自动识别结节，从而消除了人工干预。在所有实验中，所有初始轮廓都被自动定义为靠近结节的边界，这是IFACM的一项优势。而且，该方法可以有效地在单个图像中分割多个结节。