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
Descripción
Sumario: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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