Open Access Research

The whole mesh deformation model: a fast image segmentation method suitable for effective parallelization

Przemyslaw Lenkiewicz12*, Manuela Pereira1, Mário M Freire1 and José Fernandes3

Author Affiliations

1 Instituto de Telecomunicacões, Department of Computer Science, University of Beira Interior, Rua Marquês d’Ávila e Bolâma, P-6201-001 Covilhã, Portugal

2 Max Planck Institute for Psycholinguistics, Wundtlaan 1, 6525 XD, Nijmegen, The Netherlands

3 Microsoft Portugal, Av. Prof. Doutor Anibal Cavaco Silva, Tagus Park, 2744-010 Porto Salvo, Portugal

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EURASIP Journal on Advances in Signal Processing 2013, 2013:55  doi:10.1186/1687-6180-2013-55

Published: 19 March 2013

Abstract

In this article, we propose a novel image segmentation method called the whole mesh deformation (WMD) model, which aims at addressing the problems of modern medical imaging. Such problems have raised from the combination of several factors: (1) significant growth of medical image volumes sizes due to increasing capabilities of medical acquisition devices; (2) the will to increase the complexity of image processing algorithms in order to explore new functionality; (3) change in processor development and turn towards multi processing units instead of growing bus speeds and the number of operations per second of a single processing unit. Our solution is based on the concept of deformable models and is characterized by a very effective and precise segmentation capability. The proposed WMD model uses a volumetric mesh instead of a contour or a surface to represent the segmented shapes of interest, which allows exploiting more information in the image and obtaining results in shorter times, independently of image contents. The model also offers a good ability for topology changes and allows effective parallelization of workflow, which makes it a very good choice for large datasets. We present a precise model description, followed by experiments on artificial images and real medical data.

Keywords:
Segmentation; Parallelization; Deformable models; Medical imaging