# Accelerating Biological Spatial Cluster Analysis with the Parallel Integral Image Technique

arXiv: https://arxiv.org/abs/2410.16291 · HTML: https://mlsanity.com/paper/2410.16291/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 6 references that are in the map.
- First posted on arXiv 6 Oct 2024.

## Builds on (most cited first, 1 of 1)

- [Parallel approach to sliding window sums](https://mlsanity.com/paper/1811.10074/) (2018)

## Built on by (most cited first, 1 of 1)

- [PGT-I: Scaling Spatiotemporal GNNs with Memory-Efficient Distributed Training](https://mlsanity.com/paper/2507.11683/) (2025)

## Abstract (arXiv)

Spatial cluster analysis (SCA) offers valuable insights into biological images; a common SCA technique is sliding window analysis (SWA). Unfortunately, SWA's computational cost hinders its application to larger images, limiting its use to small-scale images. With advancements in high-resolution microscopy, images now exceed the capabilities of previous SWA approaches, reaching sizes up to 70,000 by 85,000 pixels. To overcome these limitations, this paper introduces the parallel integral image approach to SWA, surpassing previous methods. We achieve a remarkable speedup of 131,806x on small-scale images and consistent speedups of over 10,000x on a variety of large-scale microscopy images. We analyze the computational complexity advantages of the parallel integral image approach and present experimental results that validate the superior performance of integral-image-based methods. Our approach is made available as an open-source Python PIP package available at https://github.com/OckermanSethGVSU/BioPII.