Objectives: Speckle noise is the main factor that degrades ultrasound image contrast and segmentation failure. Determining an effective filter can reduce speckle noise and improve segmentation performances. The aim of this study was to define a useful filter to improve the segmentation outcome. Methods: Twelve filters, including median, hybrid median (Hmed), Fourier Butterworth, Fourier ideal, wavelet (Wlet), homomorphic Fourier Butterworth, homomorphic Fourier ideal, homomorphic wavelet (Hmp_Wlet), frost, anisotropic diffusion, probabilistic patch-based (PPB), and homogeneous area filters, were used to find the best filter(s) to prepare thyroid nodule segmentation. A receiver operating characteristic (ROC) analysis was used for filter evaluation in the nodule segmentation process. Accordingly, 10 morphologic parameters were measured from segmented regions to find the best parameters that predict the segmentation performance.Results: The best segmentation performance was reached by using 4 hybrid filters that mainly contain contrast-limited adaptive histogram equalization, Wlet, Hmed, Hmp_Wlet, and PPB filters. The area under the ROC curve for these filters ranged from 0.900 to 0.943 in comparison with the original image, with an area under the curve of 0.685. From 10 morphologic parameters, the area, convex area, equivalent diameter, solidity, and extent can evaluate segmentation performance.Conclusions: Hybrid filters that contain contrast-limited adaptive histogram equalization, Wlet, Hmed, Hmp_Wlet, and PPB filters have a high potential to provide good conditions for thyroid nodule segmentation in ultrasound images. In addition to an ROC analysis, morphometry of a segmented region can be used to evaluate segmentation performances.