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

arXiv: https://arxiv.org/abs/1704.00036 · HTML: https://mlsanity.com/paper/1704.00036/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Mar 2017.

## Built on by (most cited first, 4 of 4)

- [Unsupervised Deformable Image Registration with Absent Correspondences in Pre-operative and Post-Recurrence Brain Tumor MRI Scans](https://mlsanity.com/paper/2206.03900/) (2022)
- [Preserving Tumor Volumes for Unsupervised Medical Image Registration](https://mlsanity.com/paper/2309.10153/) (2023)
- [Co-Learning Semantic-aware Unsupervised Segmentation for Pathological Image Registration](https://mlsanity.com/paper/2310.11040/) (2023)
- [Spot the Difference: Detection of Topological Changes via Geometric Alignment](https://mlsanity.com/paper/2106.08233/) (2021)

## Abstract (arXiv)

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.