Increasing the speed of medical image processing in MatLab(®)

MatLab(®) has often been considered an excellent environment for fast algorithm development but is generally perceived as slow and hence not fit for routine medical image processing, where large data sets are now available e.g., high-resolution CT image sets with typically hundreds of 512x512 slices...

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Detalles Bibliográficos
Autores principales: Bister, M, Yap, CS, Ng, KH, Tok, CH
Formato: Texto
Lenguaje:English
Publicado: Department of Biomedical Imaging, Faculty of Medicine, University of Malaya, Malaysia 2007
Materias:
How I Do It
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3097656/
https://www.ncbi.nlm.nih.gov/pubmed/21614269
http://dx.doi.org/10.2349/biij.3.1.e9
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author Bister, M
Yap, CS
Ng, KH
Tok, CH
author_facet Bister, M
Yap, CS
Ng, KH
Tok, CH
author_sort Bister, M
collection PubMed
description MatLab(®) has often been considered an excellent environment for fast algorithm development but is generally perceived as slow and hence not fit for routine medical image processing, where large data sets are now available e.g., high-resolution CT image sets with typically hundreds of 512x512 slices. Yet, with proper programming practices – vectorization, pre-allocation and specialization – applications in MatLab(®) can run as fast as in C language. In this article, this point is illustrated with fast implementations of bilinear interpolation, watershed segmentation and volume rendering.
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spelling pubmed-30976562011-05-24 Increasing the speed of medical image processing in MatLab(®) Bister, M Yap, CS Ng, KH Tok, CH Biomed Imaging Interv J How I Do It MatLab(®) has often been considered an excellent environment for fast algorithm development but is generally perceived as slow and hence not fit for routine medical image processing, where large data sets are now available e.g., high-resolution CT image sets with typically hundreds of 512x512 slices. Yet, with proper programming practices – vectorization, pre-allocation and specialization – applications in MatLab(®) can run as fast as in C language. In this article, this point is illustrated with fast implementations of bilinear interpolation, watershed segmentation and volume rendering. Department of Biomedical Imaging, Faculty of Medicine, University of Malaya, Malaysia 2007-01-01 /pmc/articles/PMC3097656/ /pubmed/21614269 http://dx.doi.org/10.2349/biij.3.1.e9 Text en © 2007 Biomedical Imaging and Intervention Journal http://creativecommons.org/licenses/by/2.5/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle How I Do It
Bister, M
Yap, CS
Ng, KH
Tok, CH
Increasing the speed of medical image processing in MatLab(®)
title Increasing the speed of medical image processing in MatLab(®)
title_full Increasing the speed of medical image processing in MatLab(®)
title_fullStr Increasing the speed of medical image processing in MatLab(®)
title_full_unstemmed Increasing the speed of medical image processing in MatLab(®)
title_short Increasing the speed of medical image processing in MatLab(®)
title_sort increasing the speed of medical image processing in matlab(®)
topic How I Do It
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3097656/
https://www.ncbi.nlm.nih.gov/pubmed/21614269
http://dx.doi.org/10.2349/biij.3.1.e9
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