Cuda Based Fuzzy C-Means Acceleration for the Segmentation of Images with Fungus Grown in Foam Matrices

Rowińska, Zdzisława; Gocławski, Jarosław

doi:10.2478/v10248-012-0046-7

Cited by 19 publications

(7 citation statements)

References 3 publications

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“…The final step is done by calculating the distance between points of data and centroids. These steps are repeated until the difference of the summation of distance between points and centers is equal or less than error threshold ( ϵ ) 24 . This step is calculated by Equation .…”

Section: Methodology and Proposed Approachmentioning

confidence: 99%

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Accelerating white blood cells image segmentation using GPUs

Baker

Alsmirat

Balhaf

et al. 2019

Concurrency and Computation

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White Blood Cell (WBC) segmentation is one of the important topics in the medical image processing field. Many researchers proposed several clustering approaches to segment WBC from blood smear microscopic images. However, a fast and robust segmentation of WBCs is still a challenging task. In this work, we propose parallel algorithms that utilize the parallelism capabilities of the Graphics Processing Units (GPUs) to accelerate the segmentation of WBC from microscopic images. In this research, we implement the main image segmentation clustering algorithms using one thread that we run on a single CPU (sequential implementation) and using multiple threads that we run on both the CPU and the GPU (hybrid CPU-GPU). We focus our work on the most common four segmentation algorithms: Standard K-means (SKM), Adaptive K-means (AKM), Fuzzy C-means (FCM), and Fuzzy Possibilistic C-means (FPCM). We implement these algorithms and the pre-processing steps for WBC image segmentation in CUDA programming to take the advantages of the large number of cores in GPUs. In this work, our hybrid implementation accelerated the four studied sequential algorithms by 4X, 3.8X, 3.4X, and 3.4X, respectively, without affecting WBC segmentation quality.

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Section: Methodology and Proposed Approachmentioning

confidence: 99%

“…Rowińska and Gocławski 24 implemented Fuzzy C‐means clustering algorithm using parallel computing. They used CUDA as the programming language and converted the sequential CPU code of FCM algorithm to a parallel code.…”

Section: Related Workmentioning

confidence: 99%

Accelerating white blood cells image segmentation using GPUs

Baker

Alsmirat

Balhaf

et al. 2019

Concurrency and Computation

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“…Review of GPUs applied to RNA-Seq on cancer [163] such as parallel construction of Fuzzy C-Means clustering algorithm [164]…”

Section: Hpc Solutionsmentioning

confidence: 99%

Review of applications of high-throughput sequencing in personalized medicine: barriers and facilitators of future progress in research and clinical application

Lightbody

Haberland

Browne

et al. 2019

Briefings in Bioinformatics

136

100

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There has been an exponential growth in the performance and output of sequencing technologies (omics data) with full genome sequencing now producing gigabases of reads on a daily basis. These data may hold the promise of personalized medicine, leading to routinely available sequencing tests that can guide patient treatment decisions. In the era of high-throughput sequencing (HTS), computational considerations, data governance and clinical translation are the greatest rate-limiting steps. To ensure that the analysis, management and interpretation of such extensive omics data is exploited to its full potential, key factors, including sample sourcing, technology selection and computational expertise and resources, need to be considered, leading to an integrated set of high-performance tools and systems. This article provides an up-to-date overview of the evolution of HTS and the accompanying tools, infrastructure and data management approaches that are emerging in this space, which, if used within in a multidisciplinary context, may ultimately facilitate the development of personalized medicine.

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“…Parallelized FCM on general images gives 11 times faster than the conventional central processing unit (CPU) . A segmentation method has been developed for polyurethane foam with fungus color images and was compared with the sequential FCM implemented using C++ and MATLAB . They achieved 10‐fold speedup of their parallel proposal compared with the FCM implemented in C++ for an object area of 310k pixels, and a 50‐ to 100‐fold speedup compared with the FCM implemented in MATLAB for an object area of 260k pixels.…”

Section: Related Workmentioning

confidence: 99%

A rapid knowledge‐based partial supervision fuzzy c‐means for brain tissue segmentation with CUDA‐enabled GPU machine

Sriramakrishnan

Kalaiselvi

Somasundaram

et al. 2019

Int J Imaging Syst Tech

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The proposed work aims to quicken the magnetic resonance imaging (MRI) brain tissue segmentation process using knowledge‐based partial supervision fuzzy c‐means (KPSFCM) with graphics processing unit (GPU). The proposed KPSFCM contains three steps: knowledge‐based initialization, modification, and optimization. The knowledge‐based initialization step extracts initial centers from input MR images for KPSFCM using Gaussian‐based histogram smoothing. The modification step changes the membership function of PSFCM, which is guided by the labeled patterns of cerebrospinal fluid portion. Finally, the optimization step is achieved through size‐based optimization (SBO), adjacency‐based optimization (ABO), and parallelism‐based optimization (PBO). SBO and ABO are algorithmic level optimization techniques in central processing unit (CPU), whereas PBO is a hardware level optimization technique implemented in GPU using compute unified device architecture (CUDA). Performance of the KPSFCM is tested with online and clinical datasets. The proposed KPSFCM gives better segmentation accuracy than 14 state‐of‐the‐art‐methods, but computationally expensive. When the optimization techniques (SBO and ABO) were included, the execution time reduces by 13 times in CPU. Finally, the inclusion of PBO yields 19 times faster than the optimized CPU implementation.

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Cuda Based Fuzzy C-Means Acceleration for the Segmentation of Images with Fungus Grown in Foam Matrices

Abstract: Abstract. In the paper authors verify the ad- The authors proposed a method using CUDA programming tools, which significantly speedsup FCM computations with multiple cores built in a graphic card.

Cited by 19 publications

References 3 publications

Accelerating white blood cells image segmentation using GPUs

Accelerating white blood cells image segmentation using GPUs

Review of applications of high-throughput sequencing in personalized medicine: barriers and facilitators of future progress in research and clinical application

A rapid knowledge‐based partial supervision fuzzy c‐means for brain tissue segmentation with CUDA‐enabled GPU machine

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