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Feature-based fuzzy connectedness segmentation of ultrasound images with an object completion step

机译：具有目标完成步骤的超声图像基于特征的模糊连接分割

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摘要

Medical ultrasound (US) image segmentation and quantification can be challenging due to signal dropouts, missing boundaries, and presence of speckle, which gives images of similar objects quite different appearance. Typically, purely intensity-based methods do not lead to a good segmentation of the structures of interest. Prior work has shown that local phase and feature asymmetry, derived from the monogenic signal, extract structural information from US images. This paper proposes a new US segmentation approach based on the fuzzy connectedness framework. The approach uses local phase and feature asymmetry to define a novel affinity function, which drives the segmentation algorithm, incorporates a shape-based object completion step, and regularises the result by mean curvature flow. To appreciate the accuracy and robustness of the methodology across clinical data of varying appearance and quality, a novel entropy-based quantitative image quality assessment of the different regions of interest is introduced. The new method is applied to 81 US images of the fetal arm acquired at multiple gestational ages, as a means to define a new automated image-based biomarker of fetal nutrition. Quantitative and qualitative evaluation shows that the segmentation method is comparable to manual delineations and robust across image qualities that are typical of clinical practice. (C) 2015 The Authors. Published by Elsevier B.V.
机译：由于信号丢失，边界缺失和斑点的出现，医学超声（US）图像的分割和量化可能具有挑战性，这使相似对象的图像外观完全不同。通常，仅基于强度的方法不会导致对目标结构的良好分割。先前的工作表明，源自单基因信号的局部相位和特征不对称会从US图像中提取结构信息。本文提出了一种基于模糊连通性框架的美国分割新方法。该方法使用局部相位和特征不对称性来定义一个新颖的亲和力函数，该函数驱动分割算法，合并基于形状的对象完成步骤，并通过平均曲率流对结果进行正则化。为了欣赏该方法在不同外观和质量的临床数据上的准确性和鲁棒性，我们引入了一种基于熵的新型感兴趣区域定量图像质量评估方法。该新方法适用于在多个胎龄获得的81幅美国胎儿胎儿图像，作为一种定义新的基于图像的自动化胎儿营养生物标记物的方法。定量和定性评估表明，分割方法可与手动描述相媲美，并且在临床实践中具有跨图像质量的鲁棒性。 （C）2015作者。由Elsevier B.V.发布

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