Feature-Centered First Order Structure Tensor Scale-Space in 2D and 3D

TL;DR

This paper introduces a simplified, more accurate first order structure tensor scale-space method for 2D and 3D image analysis, reducing parameter dependence and improving feature size estimation.

Contribution

It presents a novel approach linking derivative filter width directly to feature size and introduces a ring-filter step for better edge response, enhancing accuracy over traditional methods.

Findings

More accurate feature size estimation in 2D and 3D

Reduced user parameter dependence

Out-of-the-box applicability for structural analysis

Abstract

The structure tensor method is often used for 2D and 3D analysis of imaged structures, but its results are in many cases very dependent on the user's choice of method parameters. We simplify this parameter choice in first order structure tensor scale-space by directly connecting the width of the derivative filter to the size of image features. By introducing a ring-filter step, we substitute the Gaussian integration/smoothing with a method that more accurately shifts the derivative filter response from feature edges to their center. We further demonstrate how extracted structural measures can be used to correct known inaccuracies in the scale map, resulting in a reliable representation of the feature sizes both in 2D and 3D. Compared to the traditional first order structure tensor, or previous structure tensor scale-space approaches, our solution is much more accurate and can serve as an out-of-the-box method for extracting a wide range of structural parameters with minimal user input.