PDF(Pubmed)
Abstract：
Ultrasonography is widely used to screen thyroid tumors because it is safe, easy to use, and low-cost. However, it is simultaneously affected by speckle noise and other artifacts, so early detection of thyroid abnormalities becomes difficult for the radiologist. Therefore, various researchers continuously address the limitations of sonography and improve the diagnosis potential of US images for thyroid tissue from the last three decays. Accordingly, the present study extensively reviewed various CAD systems used to classify thyroid tumor US (TTUS) images related to datasets, despeckling algorithms, segmentation algorithms, feature extraction and selection, assessment parameters, and classification algorithms. After the exhaustive review, the achievements and challenges have been reported, and build a road map for the new researchers.
摘要：
超声检查被广泛用于筛查甲状腺肿瘤，因为它是安全的，易于使用,和低成本。然而,它同时受到斑点噪声和其他伪影的影响，因此，早期发现甲状腺异常对放射科医生来说变得困难。因此,各种研究人员不断解决超声检查的局限性，并提高了US图像对最近3次衰变的甲状腺组织的诊断潜力。因此，本研究广泛回顾了用于对与数据集相关的甲状腺肿瘤US（TTUS）图像进行分类的各种CAD系统，去斑点算法，分割算法，特征提取和选择，评估参数，和分类算法。经过详尽的审查，报告了成就和挑战，并为新研究人员建立路线图。
