用局部厚度快速计算2D/3D物体的球度与圆度，效率提升显著。
Fast Sphericity and Roundness approximation in 2D and 3D using Local Thickness
- 基于局部厚度算法，简化球度表面面积计算。
- 通过轮廓/表面厚度值近似圆度，避免复杂曲率计算。
- 适用于大规模显微图像中多物体的高效形态分析。
球度和圆度是评估二维与三维图像中物体均匀性的基本度量。然而，严格定义下的计算成本较高。随着二维与三维显微成像数据集规模不断扩大，对能高效量化大量物体的算法需求日益增长。本文提出一种新方法，基于局部厚度算法输出来提取球度与圆度。对于球度，通过将物体建模为不同长宽的椭球体/椭圆，利用平均局部厚度简化表面积计算；对于圆度，通过轮廓或物体表面的局部厚度值近似，避免复杂的角点曲率计算过程。所提方法在保持与精确度量高度一致的前提下，显著优于现有实现的运行速度。
原文摘要 · Abstract (English)
Sphericity and roundness are fundamental measures used for assessing object uniformity in 2D and 3D images. However, using their strict definition makes computation costly. As both 2D and 3D microscopy imaging datasets grow larger, there is an increased demand for efficient algorithms that can quantify multiple objects in large volumes. We propose a novel approach for extracting sphericity and roundness based on the output of a local thickness algorithm. For sphericity, we simplify the surface area computation by modeling objects as spheroids/ellipses of varying lengths and widths of mean local thickness. For roundness, we avoid a complex corner curvature determination process by approximating it with local thickness values on the contour/surface of the object. The resulting methods provide an accurate representation of the exact measures while being significantly faster than their existing implementations.
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