Automatic hippocampus localization in histological images using Differential Evolution-based deformable models

Authors:
Pablo Mesejo;Roberto Ugolotti;Ferdinando Di Cunto;Mario Giacobini;Stefano Cagnoni
Affiliations:
Department of Information Engineering, University of Parma, Parma, Italy;Department of Information Engineering, University of Parma, Parma, Italy;Molecular Biotechnology Center, University of Torino, Italy;Department of Veterinary Sciences, University of Torino, Italy and Molecular Biotechnology Center, University of Torino, Italy;Department of Information Engineering, University of Parma, Parma, Italy
Venue:
Pattern Recognition Letters
Year:
2013

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Abstract

In this paper, the localization of structures in biomedical images is considered as a multimodal global continuous optimization problem and solved by means of soft computing techniques. We have developed an automatic method aimed at localizing the hippocampus in histological images, after discoveries indicating the relevance of structural changes of this region as early biomarkers for Alzheimer's disease and epilepsy. The localization is achieved by searching the parameters of an empirically-derived deformable model of the hippocampus which maximize its overlap with the corresponding anatomical structure in histological brain images. The comparison between six real-parameter optimization techniques (Levenberg-Marquardt, Differential Evolution, Simulated Annealing, Genetic Algorithms, Particle Swarm Optimization and Scatter Search) shows that Differential Evolution significantly outperforms the other techniques in this task, providing successful localizations in 90.9% and 93.0% of two test sets of real and synthetic images, respectively.