# PointSSIM: A novel low dimensional resolution invariant image-to-image comparison metric

arXiv: https://arxiv.org/abs/2506.23833 · HTML: https://mlsanity.com/paper/2506.23833/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 30 references that are in the map.
- First posted on arXiv 30 Jun 2025.

## Builds on (most cited first, 2 of 2)

- [A Survey on Deep Learning in Medical Image Analysis](https://mlsanity.com/paper/1702.05747/) (2017)
- [An Introduction to Variational Autoencoders](https://mlsanity.com/paper/1906.02691/) (2019)

## Abstract (arXiv)

This paper presents PointSSIM, a novel low-dimensional image-to-image comparison metric that is resolution invariant. Drawing inspiration from the structural similarity index measure and mathematical morphology, PointSSIM enables robust comparison across binary images of varying resolutions by transforming them into marked point pattern representations. The key features of the image, referred to as anchor points, are extracted from binary images by identifying locally adaptive maxima from the minimal distance transform. Image comparisons are then performed using a summary vector, capturing intensity, connectivity, complexity, and structural attributes. Results show that this approach provides an efficient and reliable method for image comparison, particularly suited to applications requiring structural analysis across different resolutions.