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Variational shape prior segmentation with an initial curve based on image registration technique

机译:基于图像配准技术的具有初始曲线的变体形状先验分割

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摘要

In general images, it is practically hard to distinguish only the desired object using the conventional image segmentation methods. In many cases, we can segment the desired object by using the shape information of the object in addition to the standard image segmentation. Chan and Zhu's model is not robust to the intensity changes of objects. In this paper, we propose a novel model for the shape prior segmentation that produces robust results using the hierarchical image segmentation and an attraction term. Moreover, we adopt an image registration technique and a multi-region image segmentation to get an initial for a given shape prior. Finally, we consider the free-form deformation in obtaining the shape function from the reference shape prior for real-world images. Numerical experiments demonstrate the results independent of intensities of objects and the location of the reference shape prior. All numerical calculations are automatic and progress without any user input. (C) 2019 Elsevier B.V. All rights reserved.
机译:在一般图像中,使用常规图像分割方法实际上很难仅区分所需对象。在许多情况下,除了标准图像分割外,我们还可以通过使用对象的形状信息来分割所需对象。 Chan和Zhu的模型对物体的强度变化并不稳健。在本文中,我们提出了一种用于形状先验分割的新颖模型,该模型使用分层图像分割和吸引项来产生鲁棒的结果。此外,我们采用图像配准技术和多区域图像分割来获得给定形状的初始形状的初始值。最后,我们考虑了在从真实世界图像的参考形状中获取形状函数时的自由变形。数值实验证明了结果与物体强度和先验参考形状的位置无关。所有数值计算都是自动进行的,无需任何用户输入。 (C)2019 Elsevier B.V.保留所有权利。

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