Abstract
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.
Abstract (translated)
球形度和圆度是评估二维和三维图像中对象均匀性的重要指标。然而，根据其严格的定义进行计算会非常耗时。随着二维和三维显微成像数据集的不断增大，对能够高效量化大量体积内多个物体特性的算法需求也在增加。我们提出了一种基于局部厚度算法输出来提取球形度和圆度的新方法。 对于球形度，我们简化了表面积计算过程，将对象建模为不同长度和宽度（以平均局部厚度为准）的椭球体/椭圆形，从而提高了效率。在处理圆度时，为了避免复杂的角落曲率确定过程，我们将它近似为物体轮廓或表面上的局部厚度值。这两种方法能够提供与现有算法相同准确性的结果，但计算速度显著提高。 这种方法不仅适用于二维图像中的对象，也能高效应用于三维成像数据中的复杂结构分析，在处理大量体积和多个物体的场景下尤为有效。
URL
https://arxiv.org/abs/2504.05808
