Segmentation of histological images using a metaheuristic-based level set approach - Inria - Institut national de recherche en sciences et technologies du numérique Access content directly
Conference Papers Year : 2013

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

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.
Fichier principal
Vignette du fichier
MedGec_noCopyright.pdf (7.84 Mo) Télécharger le fichier
Origin : Files produced by the author(s)
Loading...

Dates and versions

hal-01221602 , version 1 (28-10-2015)

Identifiers

Cite

Pablo Mesejo, Stefano Cagnoni, Alessandro Costalunga, Davide Valeriani. Segmentation of histological images using a metaheuristic-based level set approach. 15th Genetic and Evolutionary Computation Conference companion (GECCO’13), Jul 2013, Amsterdam, Netherlands. pp.1455-1462, ⟨10.1145/2464576.2466808⟩. ⟨hal-01221602⟩
61 View
114 Download

Altmetric

Share

Gmail Facebook X LinkedIn More