Groupwise non-rigid image alignment using few parameters: registration of facial and medical images

Ahmad Hashim Aal-Yhia*, Bernard Tiddeman, Paul Malcolm, Reyer Zwiggelaar

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingChapter


Groupwise non-rigid image alignment is a difficult non-linear optimization problem involving many parameters and often large datasets. Previous methods have explored various metrics and optimization strategies. Good results have been previously achieved with simple metrics, requiring complex optimization, often with many unintuitive parameters that require careful tuning for each dataset. In this chapter, the problem is restructured to use a simpler, iterative optimization algorithm, with very few free parameters. The warps are refined using an iterative Levenberg-Marquardt minimization to the mean, based on updating the locations of a small number of points and incorporating a stiffness constraint. This optimization approach is efficient, has very few free parameters to tune, and the authors show how to tune the few remaining parameters. Results show that the method reliably aligns various datasets including two facial datasets and two medical datasets of prostate and brain MRI images and demonstrates efficiency in terms of performance and a reduction of the computational cost.

Original languageEnglish
Title of host publicationResearch Anthology on Improving Medical Imaging Techniques for Analysis and Intervention
PublisherIGI Global Publishing
Number of pages24
ISBN (Electronic)9781668475454
ISBN (Print)9781668475447
Publication statusPublished - 09 Sept 2022


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