MatLab® has often been considered a

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 eg, highresolution
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.
© 2007 biomedical imaging and intervention journal. all rights reserved.

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., highresolution
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.
© 2007 Biomedical Imaging and Intervention Journal. All rights reserved.

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., highresolution
CT image sets with hundreds of typically 512 X 512 slices. Yet, with proper programming practices -
vectorization,Pre-allocation and specialization in MatLab® - applications 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.
© 2007 Biomedical Imaging and Intervention Journal.All rights reserved.