# Efficient Registration of Pathological Images: A Joint PCA/Image-Reconstruction Approach

## TL;DR

This paper introduces a fast, integrated PCA-based method for registering pathological images that avoids the drawbacks of low-rank/sparse decomposition, improving efficiency and tissue preservation.

## Contribution

It presents a novel joint PCA/image-reconstruction approach that efficiently removes pathologies without blurring normal tissue, outperforming traditional LRS methods.

## Key findings

- Effective on synthetic and BRATS 2015 data
- Faster and less memory-intensive than LRS
- Preserves normal tissue details during registration

## Abstract

Registration involving one or more images containing pathologies is challenging, as standard image similarity measures and spatial transforms cannot account for common changes due to pathologies. Low-rank/Sparse (LRS) decomposition removes pathologies prior to registration; however, LRS is memory-demanding and slow, which limits its use on larger data sets. Additionally, LRS blurs normal tissue regions, which may degrade registration performance. This paper proposes an efficient alternative to LRS: (1) normal tissue appearance is captured by principal component analysis (PCA) and (2) blurring is avoided by an integrated model for pathology removal and image reconstruction. Results on synthetic and BRATS 2015 data demonstrate its utility.