Intuitionistic based segmentation of thyroid nodules in ultrasound images

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.