Accelerating Biological Spatial Cluster Analysis with the Parallel Integral Image Technique

TL;DR

This paper introduces a parallel integral image technique for spatial cluster analysis in biological images, significantly accelerating computation and enabling analysis of ultra-large microscopy images.

Contribution

The paper presents a novel parallel integral image method for sliding window analysis, achieving unprecedented speedups and scalability for large biological images.

Findings

Speedup of 131,806x on small images

Over 10,000x speedup on large microscopy images

Validated superior performance through experimental analysis

Abstract

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.