Segmentation of histological images using a metaheuristic-based level set approach

Authors:
Pablo Mesejo;Stefano Cagnoni;Alessandro Costalunga;Davide Valeriani
Affiliations:
Department of Information Engineering, University of Parma, Parma, Italy;Department of Information Engineering, University of Parma, Parma, Italy;Department of Information Engineering, University of Parma, Parma, Italy;Department of Information Engineering, University of Parma, Parma, Italy
Venue:
Proceedings of the 15th annual conference companion on Genetic and evolutionary computation
Year:
2013

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Abstract

This paper presents a two-phase method to segment the hippocampus in histological images. The first phase represents a training stage where, from a training set of manually labelled images, the hippocampus representative shape and texture are derived. The second one, the proper segmentation, uses a metaheuristic to evolve the contour of a geometric deformable model using region and texture information. Three different metaheuristics (real-coded GA, Particle Swarm Optimization and Differential Evolution) and two classical segmentation algorithms (Chan & Vese model and Geodesic Active Contours) were compared over a test set of 10 histological images. The best results were attained by the real-coded GA, achieving an average and median Dice Coefficient of 0.72 and 0.77, respectively.