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An Interactive Segmentation Algorithm for Thyroid Nodules in Ultrasound Images

机译:超声图像中甲状腺结节的交互式分割算法

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Thyroid disease is extremely common and of concern because of the risk of malignancies and hyper-function and they may become malignant if not diagnosed at the right time. Ultrasound is one of the most often used methods for thyroid nodule detection. However, node detection is very difficult in ultrasound images due to their flaming nature and low quality. In this paper, an algorithm for the formalization of the contour of the nodule using the variance reduction statistic is proposed where cut points are determined, then a method of selecting the nearest neighbor points which form the shape of the nodule is generated, later B-spline method is applied to improve the accuracy of the curve shape. The extracted results are been compared with graph_cut and watershed methods for efficiency. Experiments show that the algorithm can improve the accuracy of the appearance of modality and maximum significance of data in the images is also protected.
机译:由于存在恶性肿瘤和功能亢进的危险,甲状腺疾病极为普遍,值得关注,如果在正确的时间诊断出甲状腺疾病,它们可能会变得恶性。超声是甲状腺结节检测最常用的方法之一。然而,由于超声图像的燃烧特性和低质量,在超声图像中节点检测非常困难。在本文中,提出了一种使用方差减少统计量对结节轮廓进行形式化的算法,该算法确定了切点,然后生成一种选择形成结节形状的最近邻点的方法,然后进行B-样条法用于提高曲线形状的精度。将提取的结果与graph_cut和分水岭方法进行比较以提高效率。实验表明,该算法可以提高模态出现的准确性,并且可以保护图像中数据的最大显着性。

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