The influence of gradient estimation on the extraction of boundary point cloud

Qian Huang, T. Wischgoll
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引用次数: 0

Abstract

To extract a tubular object boundary from a volumetric image is important to compute its morphometric properties, like the estimation of the boundary curvature, or the radius of a tubular object, for example, the radius is one of the descriptions of blood vessel for detecting the soft plaque. How to estimate the gradient of the volumetric data has an influence on the computation results of the morphometric properties. Extract the points of maximum gradient along the gradient direction in 3D as the boundary point cloud of an object is used by [4]. The boundary points are computed by trilinearly interpolated the volumetric datasets, and apply the parabolic interpolation to find the maximum gradient along the gradient direction. The extraction of boundary point cloud depends on the estimation of the image gradients. This paper is to compare the tricubic B-spline and trilinear interpolation algorithms on the estimations of morphometric properties of the volumetric dataset.
梯度估计对边界点云提取的影响
从体积图像中提取管状物体的边界是计算其形态特征的关键,如边界曲率的估计或管状物体的半径,例如半径是检测软斑块时血管的描述之一。如何估计体积数据的梯度,直接影响到形态计量学性质的计算结果。使用[4]作为物体的边界点云,在三维中沿梯度方向提取梯度最大的点。通过对体积数据集进行三线性插值计算边界点,并应用抛物线插值求沿梯度方向的最大梯度。边界点云的提取依赖于图像梯度的估计。本文比较了三次b样条插值算法和三线性插值算法对体积数据集形态特征估计的影响。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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