Implementation of medical image segmentation in CUDA

As the fast development of GPU, people tend to use it for more general purposes than its original graphic related work. The high parallel computation capabilities of GPU are welcomed by programmers who work at medical image processing which always have to deal with a large scale of voxel computation...

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Bibliographic Details
Published in2008 International Conference on Information Technology and Applications in Biomedicine pp. 82 - 85
Main Authors Lei Pan, Lixu Gu, Jianrong Xu
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.05.2008
Subjects
Application software
Biomedical imaging
Computer applications
Computer architecture
Computer interfaces
Concurrent computing
CUDA
GPU
Graphics
Image segmentation
Large-scale systems
Region Growing
Segmentation
Watershed
Yarn
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ISBN9781424422548
142442254X
ISSN2168-2194
DOI10.1109/ITAB.2008.4570542

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Summary:As the fast development of GPU, people tend to use it for more general purposes than its original graphic related work. The high parallel computation capabilities of GPU are welcomed by programmers who work at medical image processing which always have to deal with a large scale of voxel computation. The birth of NVIDIAreg CUDAtrade technology and CUDA-enabled GPUs brought a revolution in the general purpose GPU area. In this paper, we propose the implementation of several medical image segmentation algorithms using CUDA and CUDA-enabled GPUs, compare their performance and results to the previous implementation in old version of GPU and CPU, indicate the advantages of using CUDA technology and how to design algorithm to make full use of it.
ISBN:9781424422548
142442254X
ISSN:2168-2194
DOI:10.1109/ITAB.2008.4570542