Bayesian Iterative Closest Point for Shape Analysis of Brain Structures

Mauricio Castaño-Aguirre, Mauricio Castaño-Aguirre, Hernan F. Garcia, Hernan F. Garcia, Álvaro Orozco, Gloria Porras-Hurtado, David Cárdenas-Peña

2023

Abstract

Machine learning in medical image analysis has proved to be a strategy that solves many problems emerging from the variability in the physician’s outlines and the amount of time each physician spends analyzing each image. One of the most critical medical image analysis approaches is Medical Image Registration which has been a topic of active research for the last few years. In this paper, we proposed a Bayesian Optimization framework for Point Cloud Registration for shape analysis of brain structures. Here, we rely on a modified version of the Iterative Closest Point (ICP) algorithm. This approach built a black box function that receives input parameters for performing an Point Cloud transformation. Then, we used a similarity metric that shows the performance of the transformation. With this similarity metric, we built a function to define a Bayesian strategy that allows us to find the global optimum of the similarity metric-based function. To this end, we used Bayesian Optimization, which performs global optimization of unknown functions making observations and performing probabilistic calculations. This model considers all the previous observations, which prevents the strategy from falling into an optimal local, as often happens in strategies based on classical optimization approaches such as Gradient Descent. Finally, we evaluate the model by performing a point cloud registration process corresponding to brain structures at different time instances. The experimental results show a faster convergence towards the global optimum and building. Besides, the proposed model evidenced robust optimization results for registration strategies in point clouds.

Paper Citation

in Harvard Style

Castaño-Aguirre M., F. Garcia H., Orozco Á., Porras-Hurtado G. and Cárdenas-Peña D. (2023). Bayesian Iterative Closest Point for Shape Analysis of Brain Structures. In Proceedings of the 12th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM, ISBN 978-989-758-626-2, pages 920-925. DOI: 10.5220/0011747200003411

in Bibtex Style

@conference{icpram23,
author={Mauricio Castaño-Aguirre and Hernan F. Garcia and Álvaro Orozco and Gloria Porras-Hurtado and David Cárdenas-Peña},
title={Bayesian Iterative Closest Point for Shape Analysis of Brain Structures},
booktitle={Proceedings of the 12th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,},
year={2023},
pages={920-925},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011747200003411},
isbn={978-989-758-626-2},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 12th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,
TI - Bayesian Iterative Closest Point for Shape Analysis of Brain Structures
SN - 978-989-758-626-2
AU - Castaño-Aguirre M.
AU - F. Garcia H.
AU - Orozco Á.
AU - Porras-Hurtado G.
AU - Cárdenas-Peña D.
PY - 2023
SP - 920
EP - 925
DO - 10.5220/0011747200003411