eBangor

Tuning of Patient-Specific Deformable Models Using an Adaptive Evolutionary Optimization Strategy

Vidal, F.P. and Villard, P-F. and Lutton, E. (2012) Tuning of Patient-Specific Deformable Models Using an Adaptive Evolutionary Optimization Strategy. IEEE Journal of Biomedical Engineering, 59 ((10)). pp. 2942-2949. DOI: 10.1109/TBME.2012.2213251

Full-text not available from this repository..

Abstract

We present and analyze the behavior of an evolutionary algorithm designed to estimate the parameters of a complex organ behavior model. The model is adaptable to account for patient's specificities. The aim is to finely tune the model to be accurately adapted to various real patient datasets. It can then be embedded, for example, in high fidelity simulations of the human physiology. We present here an application focused on respiration modeling. The algorithm is automatic and adaptive. A compound fitness function has been designed to take into account for various quantities that have to be minimized. The algorithm efficiency is experimentally analyzed on several real test cases: 1) three patient datasets have been acquired with the �breath hold� protocol, and 2) two datasets corresponds to 4-D CT scans. Its performance is compared with two traditional methods (downhill simplex and conjugate gradient descent): a random search and a basic real-valued genetic algorithm. The results show that our evolutionary scheme provides more significantly stable and accurate results.

Item Type: Article
Subjects: Research Publications
Departments: College of Physical and Applied Sciences > School of Computer Science
Date Deposited: 09 Dec 2014 16:49
Last Modified: 23 Sep 2015 03:11
ISSN: 0018-9294
URI: http://e.bangor.ac.uk/id/eprint/1164
Identification Number: DOI: 10.1109/TBME.2012.2213251
Administer Item Administer Item

eBangor is powered by EPrints 3 which is developed by the School of Electronics and Computer Science at the University of Southampton. More information and software credits.