提出一种旋转等变的图像各向异性分析方法，提升医学图像分析稳定性。
EquivAnIA: A Spectral Method for Rotation-Equivariant Anisotropic Image Analysis
- 基于蛋糕小波与脊状滤波器构建频域分析框架
- 在合成与真实图像上验证对数值旋转的鲁棒性
- 适用于纹理/几何结构图像的角向配准任务
各向异性图像分析广泛应用于医学与科学成像。尽管相关研究众多，但多数方法对数值旋转的鲁棒性尚未深入探讨。理想情况下，图像旋转后其主方向与角度分布应同步旋转。本文提出一种新的频域各向异性分析方法（EquivAnIA），结合两种成熟的定向滤波器——蛋糕小波（cake wavelets）与脊状滤波器（ridge filters）。通过在含几何结构或纹理的合成及真实图像上进行大量实验，验证了该方法对数值旋转的高度鲁棒性，并成功应用于角向图像配准任务。代码已公开于 https://github.com/jscanvic/Anisotropic-Analysis。
原文摘要 · Abstract (English)
Anisotropic image analysis is ubiquitous in medical and scientific imaging, and while the literature on the subject is extensive, the robustness to numerical rotations of numerous methods remains to be studied. Indeed, the principal directions and angular profile of a rotated image are often expected to rotate accordingly. In this work, we propose a new spectral method for the anisotropic analysis of images (EquivAnIA) using two established directional filters, namely cake wavelets, and ridge filters. We show that it is robust to numerical rotations throughout extensive experiments on synthetic and real-world images containing geometric structures or textures, and we also apply it successfully for a task of angular image registration. The code is available at https://github.com/jscanvic/Anisotropic-Analysis
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