Parallel fuzzy segmentation of multiple objects

Garduño, Edgar; Herman, Gabor T.

doi:10.1002/ima.20170

Cited by 8 publications

(4 citation statements)

References 28 publications

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“…Therefore, some researchers proposed parallel implementations of FC. An OpenMP-based FC was proposed in 2008, the authors adapted a sequential fuzzy segmentation algorithm to multiprocessor machines [ 10 ]. Thereafter, Zhuge et al [ 11 ] addressed a CUDA-kFOE algorithm which is based on NVIDIA’s compute unified device architecture (CUDA) platform.…”

Section: Introductionmentioning

confidence: 99%

An improved parallel fuzzy connected image segmentation method based on CUDA

Wang

Huang

2016

BioMed Eng OnLine

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PurposeFuzzy connectedness method (FC) is an effective method for extracting fuzzy objects from medical images. However, when FC is applied to large medical image datasets, its running time will be greatly expensive. Therefore, a parallel CUDA version of FC (CUDA-kFOE) was proposed by Ying et al. to accelerate the original FC. Unfortunately, CUDA-kFOE does not consider the edges between GPU blocks, which causes miscalculation of edge points. In this paper, an improved algorithm is proposed by adding a correction step on the edge points. The improved algorithm can greatly enhance the calculation accuracy.MethodsIn the improved method, an iterative manner is applied. In the first iteration, the affinity computation strategy is changed and a look up table is employed for memory reduction. In the second iteration, the error voxels because of asynchronism are updated again.ResultsThree different CT sequences of hepatic vascular with different sizes were used in the experiments with three different seeds. NVIDIA Tesla C2075 is used to evaluate our improved method over these three data sets. Experimental results show that the improved algorithm can achieve a faster segmentation compared to the CPU version and higher accuracy than CUDA-kFOE.ConclusionsThe calculation results were consistent with the CPU version, which demonstrates that it corrects the edge point calculation error of the original CUDA-kFOE. The proposed method has a comparable time cost and has less errors compared to the original CUDA-kFOE as demonstrated in the experimental results. In the future, we will focus on automatic acquisition method and automatic processing.

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Section: Introductionmentioning

confidence: 99%

An improved parallel fuzzy connected image segmentation method based on CUDA

Wang

Huang

2016

BioMed Eng OnLine

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“…An OpenMP-based parallel implementation of the FC algorithm has been reported in Ref. 29. A speed increase of approximately five has been achieved, relative to the sequential implementation on a SGI Altix 4700; an expensive, shared memory multiprocessor system.…”

Section: Introductionmentioning

confidence: 99%

GPU‐based relative fuzzy connectedness image segmentation

et al. 2012

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Purpose: Recently, clinical radiological research and practice are becoming increasingly quantitative. Further, images continue to increase in size and volume. For quantitative radiology to become practical, it is crucial that image segmentation algorithms and their implementations are rapid and yield practical run time on very large data sets. The purpose of this paper is to present a parallel version of an algorithm that belongs to the family of fuzzy connectedness (FC) algorithms, to achieve an interactive speed for segmenting large medical image data sets. Methods:The most common FC segmentations, optimizing an ∞ -based energy, are known as relative fuzzy connectedness (RFC) and iterative relative fuzzy connectedness (IRFC). Both RFC and IRFC objects (of which IRFC contains RFC) can be found via linear time algorithms, linear with respect to the image size. The new algorithm, P-ORFC (for parallel optimal RFC), which is implemented by using NVIDIA's Compute Unified Device Architecture (CUDA) platform, considerably improves the computational speed of the above mentioned CPU based IRFC algorithm. Results: Experiments based on four data sets of small, medium, large, and super data size, achieved speedup factors of 32.8×, 22.9×, 20.9×, and 17.5×, correspondingly, on the NVIDIA Tesla C1060 platform. Although the output of P-ORFC need not precisely match that of IRFC output, it is very close to it and, as the authors prove, always lies between the RFC and IRFC objects. Conclusions: A parallel version of a top-of-the-line algorithm in the family of FC has been developed on the NVIDIA GPUs. An interactive speed of segmentation has been achieved, even for the largest medical image data set. Such GPU implementations may play a crucial role in automatic anatomy recognition in clinical radiology.

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“…An OpenMP-based parallel implementation of the fuzzy connectedness algorithm has been reported recently. 17 This algorithm has been adapted to a distributed-processing scheme. 7 This scheme also works using manager-worker paradigm in which there are several processors (the workers) handling subsets of the data set and a special processor (the manager) that controls how the other processors carry out the segmentation of their corresponding subset.…”

Section: Introductionmentioning

confidence: 99%

“…Typically, OpenMP systems are expensive, tightly coupled, shared memory multiprocessor systems (MPS), such as the SGI Altix 4700, which is the hardware being used in Ref. 17 18 to begin with and due to the differences in image data, objects segmented, and the computing platforms used.…”

Section: Introductionmentioning

confidence: 99%

Parallel fuzzy connected image segmentation on GPU

et al. 2011

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Purpose: Image segmentation techniques using fuzzy connectedness (FC) principles have shown their effectiveness in segmenting a variety of objects in several large applications. However, one challenge in these algorithms has been their excessive computational requirements when processing large image datasets. Nowadays, commodity graphics hardware provides a highly parallel computing environment. In this paper, the authors present a parallel fuzzy connected image segmentation algorithm implementation on NVIDIA's compute unified device Architecture (CUDA) platform for segmenting medical image data sets. Methods: In the FC algorithm, there are two major computational tasks: (i) computing the fuzzy affinity relations and (ii) computing the fuzzy connectedness relations. These two tasks are implemented as CUDA kernels and executed on GPU. A dramatic improvement in speed for both tasks is achieved as a result. Results: Our experiments based on three data sets of small, medium, and large data size demonstrate the efficiency of the parallel algorithm, which achieves a speed-up factor of 24.4x, 18.1x, and 10.3x, correspondingly, for the three data sets on the NVIDIA Tesla C1060 over the implementation of the algorithm on CPU, and takes 0.25, 0.72, and 15.04 s, correspondingly, for the three data sets. Conclusions: The authors developed a parallel algorithm of the widely used fuzzy connected image segmentation method on the NVIDIA GPUs, which are far more cost-and speed-effective than both cluster of workstations and multiprocessing systems. A near-interactive speed of segmentation has been achieved, even for the large data set.

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Parallel fuzzy segmentation of multiple objects

Cited by 8 publications

References 28 publications

An improved parallel fuzzy connected image segmentation method based on CUDA

An improved parallel fuzzy connected image segmentation method based on CUDA

GPU‐based relative fuzzy connectedness image segmentation

Parallel fuzzy connected image segmentation on GPU

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