Medical image segmentation with deformable models on graphics processing units

Alvarado, Rigo; Tapia, Juan J.; Rolón, Julio

doi:10.1007/s11227-013-1042-4

Cited by 8 publications

(7 citation statements)

References 29 publications

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“…CPU implementation of Euler's method. 2 The datasets were processed 10 times with Euler's method first using a fixed number of iterations and then the multigrid GPU implementation was executed for as many iterations as needed to reach the same error or lower as the Euler method. The results show that the multigrid GPU implementation is several times (1.9-5.1) faster than the Euler GPU implementation.…”

Section: Resultsmentioning

confidence: 99%

“…Eidheim et al [6], He and Kuester [11] and Zheng and Zhang [20] all presented GPU implementations of GVF and active contours for 2D images using shader languages. A GPU implementation of 2D GVF written in CUDA was done by Alvarado et al [2]. In our previous work [16], we presented a highly optimized GPU implementation of GVF for both 2D and 3D images using OpenCL.…”

Section: Introductionmentioning

confidence: 99%

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Multigrid gradient vector flow computation on the GPU

Smistad

Lindseth

2014

J Real-Time Image Proc

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Gradient vector flow (GVF) is a feature-preserving spatial diffusion of image gradients. It was introduced to overcome the limited capture range in traditional active contour segmentation. However, the original iterative solver for GVF, using Euler's method, converges very slowly. Thus, many iterations are needed to achieve the desired capture range. Several groups have investigated the use of graphic processing units (GPUs) to accelerate the GVF computation. Still, this does not reduce the number of iterations needed. Multigrid methods, on the other hand, have been shown to provide a much better capture range using considerable less iterations. However, non-GPU implementations of the multigrid method are not as fast as the Euler method when executed on the GPU. In this paper, a novel GPU implementation of a multigrid solver for GVF written in OpenCL is presented. The results show that this implementation converges and provides a better capture range about 2-5 times faster than the conventional iterative GVF solver on the GPU.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Multigrid gradient vector flow computation on the GPU

Smistad

Lindseth

2014

J Real-Time Image Proc

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“…Domanski et al [19] developed a cluster Web services (CWS) framework capable of taking advantage of massively parallel technologies composed of a PC-cluster 4 and GPUs. 5 This framework facilitated communication between the client and server through the Internet in order to balance and distribute the computational load.…”

Section: Image Reconstructionmentioning

confidence: 99%

“…Parallel imaging is a well-established acceleration technique based on the spatial sensitivity of array receivers[59] 4. 32 Intel Xeon CPU cores 5.…”

mentioning

confidence: 99%

Techniques of medical image processing and analysis accelerated by high-performance computing: a systematic literature review

Gulo

Sementille

Tavares

2017

J Real-Time Image Proc

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Techniques of medical image processing and analysis play a crucial role in many clinical scenarios, including in diagnosis and treatment planning. However, immense quantities of data and high complexity of the algorithms often used are computationally demanding. As a result, there now exists a wide range of techniques of medical image processing and analysis that require the application of high-performance computing solutions in order to reduce the required runtime. The main purpose of this review is to provide a comprehensive reference source of techniques of medical image processing and analysis that have been accelerated by high-performance computing solutions. With this in mind, the articles available in the Scopus and Web of Science electronic repositories were searched. Subsequently, the most relevant articles found were individually analyzed in order to identify: (a) the metrics used to evaluate computing performance, (b) the high-performance computing solution used, (c) the parallel design adopted, and (d) the task of medical image processing and analysis involved. Hence, the techniques of medical image processing and analysis found were identified, reviewed, and discussed, particularly in terms of computational performance. Consequently, the techniques reviewed herein present the progress made so far in reducing the computational runtime involved, and the difficulties and challenges that remain to be overcome.

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“…Level sets in particular have largely benefited from such massively parallel implementations [20][21][22]. Explicit approaches have also benefited from GPU acceleration, although to a lesser extent, with benefits mostly impacting heavy image-centric pre-processing operations, rather than the contour deformation itself [23][24][25]. Aside from such GPUcentric approaches, very few alternatives have been investigated.…”

Section: Introduction and Related Workmentioning

confidence: 99%

Quad-edge active contours for biomedical image segmentation

Gonzalez

Rohfritsch

Faure

et al. 2017

2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017)

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International audienceWe investigate a novel, parallel implementation of active contours for image segmentation combining a multi-agent system with a quad-edge representation of the contour. The control points of the contour evolve independently from one another in a parallel fashion, handling contour deformation, and convergence, while the quad-edge representation simplifies contour manipulation and local re-sampling during its evolution. We illustrate this new approach on biological images, and compare results with a conventional active contour implementation, discussing its benefits and limitations. This preliminary work is made freely available as a plug-in for our open-source Icy platform, where it will be developed with future extensions

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Medical image segmentation with deformable models on graphics processing units

Cited by 8 publications

References 29 publications

Multigrid gradient vector flow computation on the GPU

Multigrid gradient vector flow computation on the GPU

Techniques of medical image processing and analysis accelerated by high-performance computing: a systematic literature review

Quad-edge active contours for biomedical image segmentation

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