Computation of Gray Level Co-Occurrence Matrix Based on CUDA and Optimization for Medical Computer Vision Application
Various fields in medicine require scientific research and computer application. This results in computation time optimization becoming a task that is of increasing importance due to its highly parallel architecture. As is well-known, the graphics processing unit (GPU) is regarded as a powerful engi...
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doaj-f162717afae84382af58ffbb3a18f78e2021-03-29T20:28:42ZengIEEEIEEE Access2169-35362018-01-016677626777010.1109/ACCESS.2018.28776978528364Computation of Gray Level Co-Occurrence Matrix Based on CUDA and Optimization for Medical Computer Vision ApplicationHuichao Hong0https://orcid.org/0000-0003-1066-8963Lixin Zheng1https://orcid.org/0000-0002-5146-8661Shuwan Pan2Engineering Institute, Huaqiao University, Quanzhou, ChinaEngineering Institute, Huaqiao University, Quanzhou, ChinaEngineering Institute, Huaqiao University, Quanzhou, ChinaVarious fields in medicine require scientific research and computer application. This results in computation time optimization becoming a task that is of increasing importance due to its highly parallel architecture. As is well-known, the graphics processing unit (GPU) is regarded as a powerful engine for application programs that demand fairly high computation capabilities. Our study is based on the deep analysis of the parallelism pertaining to the calculation of the gray level co-occurrence matrix, whereby an algorithm was introduced to optimize the method used to compute the gray-level co-occurrence matrix (GLCM) of an image. Furthermore, strategies (e.g., copying, image partitioning, and so on) were proposed to optimize the parallel algorithm. Our experiments indicate that without losing the computational accuracy, the speed-up ratio of the GLCM computation of images with different resolutions by GPU utilizing compute unified device architecture was at least 50 times faster than that of the GLCM computation by the central processing unit. This manifestation of a significantly improved performance can lead to the development of a very useful computational tool in medical computer vision.https://ieeexplore.ieee.org/document/8528364/CUDAGPUgray-level co-occurrence matrixparallel computingmedical computer vision |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Huichao Hong Lixin Zheng Shuwan Pan |
spellingShingle |
Huichao Hong Lixin Zheng Shuwan Pan Computation of Gray Level Co-Occurrence Matrix Based on CUDA and Optimization for Medical Computer Vision Application IEEE Access CUDA GPU gray-level co-occurrence matrix parallel computing medical computer vision |
author_facet |
Huichao Hong Lixin Zheng Shuwan Pan |
author_sort |
Huichao Hong |
title |
Computation of Gray Level Co-Occurrence Matrix Based on CUDA and Optimization for Medical Computer Vision Application |
title_short |
Computation of Gray Level Co-Occurrence Matrix Based on CUDA and Optimization for Medical Computer Vision Application |
title_full |
Computation of Gray Level Co-Occurrence Matrix Based on CUDA and Optimization for Medical Computer Vision Application |
title_fullStr |
Computation of Gray Level Co-Occurrence Matrix Based on CUDA and Optimization for Medical Computer Vision Application |
title_full_unstemmed |
Computation of Gray Level Co-Occurrence Matrix Based on CUDA and Optimization for Medical Computer Vision Application |
title_sort |
computation of gray level co-occurrence matrix based on cuda and optimization for medical computer vision application |
publisher |
IEEE |
series |
IEEE Access |
issn |
2169-3536 |
publishDate |
2018-01-01 |
description |
Various fields in medicine require scientific research and computer application. This results in computation time optimization becoming a task that is of increasing importance due to its highly parallel architecture. As is well-known, the graphics processing unit (GPU) is regarded as a powerful engine for application programs that demand fairly high computation capabilities. Our study is based on the deep analysis of the parallelism pertaining to the calculation of the gray level co-occurrence matrix, whereby an algorithm was introduced to optimize the method used to compute the gray-level co-occurrence matrix (GLCM) of an image. Furthermore, strategies (e.g., copying, image partitioning, and so on) were proposed to optimize the parallel algorithm. Our experiments indicate that without losing the computational accuracy, the speed-up ratio of the GLCM computation of images with different resolutions by GPU utilizing compute unified device architecture was at least 50 times faster than that of the GLCM computation by the central processing unit. This manifestation of a significantly improved performance can lead to the development of a very useful computational tool in medical computer vision. |
topic |
CUDA GPU gray-level co-occurrence matrix parallel computing medical computer vision |
url |
https://ieeexplore.ieee.org/document/8528364/ |
work_keys_str_mv |
AT huichaohong computationofgraylevelcooccurrencematrixbasedoncudaandoptimizationformedicalcomputervisionapplication AT lixinzheng computationofgraylevelcooccurrencematrixbasedoncudaandoptimizationformedicalcomputervisionapplication AT shuwanpan computationofgraylevelcooccurrencematrixbasedoncudaandoptimizationformedicalcomputervisionapplication |
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