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We present the CPMA, a new method for medial axis pruning with noise robustness and equivariance to isometric transformations. The CPMA leverages the discrete cosine transform to create smooth versions of a shape &#x03A9;. We use the smooth shapes to compute a score function <i>F</i><sub>&#x03A9;</sub> that filters out spurious branches from the medial axis of the original shape &#x03A9;. Our method generalizes to n-dimensional shapes given the properties of the Discrete Cosine Transform. We extensively compare with state-of-the-art pruning methods to highlight the CPMA's noise robustness and isometric equivariance. We conducted experiments using two 2D datasets - Kimia216 and Animal2000 - and one 3D dataset - the Groningen benchmark. We found that our pruning approach achieves competitive results and yields stable medial axes even in scenarios with significant contour perturbations.

Cosine-Pruned Medial Axis: A New Method for Isometric Equivariant and Noise-Free Medial Axis Extraction