基于 [公式:见正文] 非张量乘积 B-样条的医学图像非刚性配准。

4区 计算机科学 Q1 Arts and Humanities
Qi Zheng, Chaoyue Liu, Jincai Chang
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引用次数: 0

摘要

本研究将非张量积 B-样条算法应用于配准过程的搜索空间,并提出了一种新的图像非刚性配准方法。张量积 B-样条是定义在 x 和 y 两个方向上的函数,而非张量积 B-样条[公式:见正文]则定义在 2 型三角剖分的四个方向上。对于某些问题,使用非张量积 B-样条来描述图像的非刚性变形,可以更准确地提取图像的四方向信息,从而在更多方向上描述图像的全局或局部非刚性变形。事实上,它提供了一种解决图像多方位变形问题的方法。此外,医学图像的感兴趣区是不规则的,通常在边界三角形上不存在值。非张量乘积 B-样条曲线在边界三角形上的基函数值仅为 0。该算法可对患者的计算机断层扫描图像和磁共振成像图像进行完全自动的非刚性配准。本研究特别比较了所提算法与张量积 B-样条曲线配准算法的性能。结果表明,所提出的算法明显提高了精确度。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Non-rigid registration of medical images based on [Formula: see text] non-tensor product B-spline.

Non-rigid registration of medical images based on [Formula: see text] non-tensor product B-spline.

Non-rigid registration of medical images based on [Formula: see text] non-tensor product B-spline.

Non-rigid registration of medical images based on [Formula: see text] non-tensor product B-spline.

In this study, a non-tensor product B-spline algorithm is applied to the search space of the registration process, and a new method of image non-rigid registration is proposed. The tensor product B-spline is a function defined in the two directions of x and y, while the non-tensor product B-spline [Formula: see text] is defined in four directions on the 2-type triangulation. For certain problems, using non-tensor product B-splines to describe the non-rigid deformation of an image can more accurately extract the four-directional information of the image, thereby describing the global or local non-rigid deformation of the image in more directions. Indeed, it provides a method to solve the problem of image deformation in multiple directions. In addition, the region of interest of medical images is irregular, and usually no value exists on the boundary triangle. The value of the basis function of the non-tensor product B-spline on the boundary triangle is only 0. The algorithm process is optimized. The algorithm performs completely automatic non-rigid registration of computed tomography and magnetic resonance imaging images of patients. In particular, this study compares the performance of the proposed algorithm with the tensor product B-spline registration algorithm. The results elucidate that the proposed algorithm clearly improves the accuracy.

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来源期刊
Visual Computing for Industry, Biomedicine, and Art
Visual Computing for Industry, Biomedicine, and Art Arts and Humanities-Visual Arts and Performing Arts
CiteScore
5.60
自引率
0.00%
发文量
28
审稿时长
5 weeks
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