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A hybrid multi-scale model for thyroid nodule boundary detection on ultrasound images.

机译:超声图像上甲状腺结节边界检测的混合多尺度模型。

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摘要

A hybrid model for thyroid nodule boundary detection on ultrasound images is introduced. The segmentation model combines the advantages of the "a trous" wavelet transform to detect sharp gray-level variations and the efficiency of the Hough transform to discriminate the region of interest within an environment with excessive structural noise. The proposed method comprise three major steps: a wavelet edge detection procedure for speckle reduction and edge map estimation, based on local maxima representation. Subsequently, a multiscale structure model is utilised in order to acquire a contour representation by means of local maxima chaining with similar attributes to form significant structures. Finally, the Hough transform is employed with 'a priori' knowledge related to the nodule's shape in order to distinguish the nodule's contour from adjacent structures. The comparative study between our automatic method and manual delineations demonstrated that the boundaries extracted by the hybrid model are closely correlated with that of the physicians. The proposed hybrid method can be of value to thyroid nodules' shape-based classification and as an educational tool for inexperienced radiologists.
机译:介绍了一种用于超声图像上甲状腺结节边界检测的混合模型。分割模型结合了“三重”小波变换的优势,可以检测出尖锐的灰度变化;而霍夫变换的效率,则可以在结构噪声过大的环境中区分出感兴趣的区域。所提出的方法包括三个主要步骤:基于局部最大值表示的用于斑点减少的小波边缘检测过程和边缘图估计。随后,利用多尺度结构模型,以便通过具有相似属性的局部最大值链接来获取轮廓表示,以形成重要的结构。最后,霍夫变换与结节形状有关的“先验”知识用于将结节的轮廓与相邻结构区分开。我们的自动方法和手动描述方法之间的比较研究表明,混合模型提取的边界与医生的边界紧密相关。所提出的混合方法对于基于甲状腺结节形状的分类具有重要的价值,并且可以作为没有经验的放射科医生的教育工具。

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