Fast Sphericity and Roundness approximation in 2D and 3D using Local Thickness
This addresses the need for faster algorithms to quantify object uniformity in large microscopy datasets, though it is incremental as it builds on existing local thickness methods.
The paper tackles the problem of efficiently computing sphericity and roundness for objects in 2D and 3D images by proposing a novel approach based on local thickness, which significantly speeds up calculations compared to existing methods.
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.