DBPapers
DOI:10.5593/SGEM2013/BB2.V1/S07.001

ANALYSIS OF MINING IMAGES USING ACTIVE CONTOURS AND SELF ORGANIZING MIGRATION ALGORITHM

L. Lichev, T. Fabian, L. Skanderova
Monday 5 August 2013 by Libadmin2013

References: 13th SGEM GeoConference on Informatics, Geoinformatics And Remote Sensing, www.sgem.org, SGEM2013 Conference Proceedings, ISBN 978-954-91818-9-0 / ISSN 1314-2704, June 16-22, 2013, Vol. 1, 3 - 10 pp

ABSTRACT

In this paper, we deal with analysis and evaluation of objects of interest which are present in ultrasound images as well as assessment of the progress or regressions that occurred in these objects. We focused on the analysis of mining images using active contours and Self-organizing migration algorithm (SOMA). Here, we describe procedures employing combination of common methods and evolutionary algorithms for recognizing points of interest in the images that may serve in determining various parameters and properties of analyzed objects. We use SOMA to optimize the energy function of deformable models used to approximate the locations and shapes of object boundaries. We suppose that SOMA can be used to find the desired global solution. Evolutionary algorithms are based on evolution principles found in nature and respect the Darwin`s theory of natural selection according to the defined cost function and gene recombination and mutation. As the computation of gradient vector flow field and also the evolution of active contour are computationally expensive, we investigate the suitability of the GPU for a parallel implementation.

Keywords: mining image, image segmentation, active contour, GVF, SOMA

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