Medical image segmentation with deformable models on graphics processing units

In this work, the parallel implementation of a segmentation algorithm based on the gradient vector flow (GVF) deformable model in a graphics processing unit (GPU) is presented. The proposed implementation focuses on the parallelization of the computation of the GVF field. In order to make a performa...

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Bibliographic Details
Published inThe Journal of supercomputing Vol. 68; no. 1; pp. 339 - 364
Main Authors Alvarado, Rigo, Tapia, Juan J., Rolón, Julio C.
Format Journal Article
LanguageEnglish
Published Boston Springer US 01.04.2014
Subjects
Algorithms
Compilers
Computation
Computer Science
Deformation
Graphics processing units
Interpreters
Mathematical models
Processor Architectures
Programming Languages
Segmentation
Shape
Texture
CUDA
Deformable models
Segmentation
OpenMP
Medical image
GPU
Snaxel reallocation
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Summary:In this work, the parallel implementation of a segmentation algorithm based on the gradient vector flow (GVF) deformable model in a graphics processing unit (GPU) is presented. The proposed implementation focuses on the parallelization of the computation of the GVF field. In order to make a performance comparison of the proposed GPU algorithm, an OpenMP-based implementation is presented too. We also present an analysis of the textures and global memory performance in the computing of the GVF field. To improve the efficiency and the performance of the active contour segmentation, a novel snaxel reallocation method is proposed. The main advantage of the reallocation process is the small linear system needed to perform the segmentation and its low computational load. To assure the convergence of the active contour deformation, we propose a stopping criterion based on the root mean square error for the iterative solution of the evolution equations.
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ISSN:0920-8542
1573-0484
DOI:10.1007/s11227-013-1042-4