
Image Kernel Convolution
Please use this identifier to cite or link to this publication: http://hdl.handle.net/1926/1323 |
Published in The Insight Journal - 2007 July - December.
Submitted by Nick Tustison on 12-23-2007.
Convolution is a basic operation in image analysis and the Insight Toolkit provides a well-framed mechanism for such operations (see Chapter 11 of the ITK Software Guide. The classes associated with this contribution are meant to simplify convolution operations for the user who wishes to convolve a given image with an arbitrary image kernel. This entails the use of the neighborhood iterator coupled with a image kernel neighborhood operator which we provide. A couple of examples are provided to illustrate its use.
Data
BrainT1SliceBorder20.nii.gz (20Kb)
constant.nii.gz (80b)
output_avg.nii.gz (53Kb)
output_sobel_x.nii.gz (53Kb)
output_sobel_y.nii.gz (47Kb)
sobel_x.nii.gz (87b)
sobel_y.nii.gz (85b)
constant.nii.gz (80b)
output_avg.nii.gz (53Kb)
output_sobel_x.nii.gz (53Kb)
output_sobel_y.nii.gz (47Kb)
sobel_x.nii.gz (87b)
sobel_y.nii.gz (85b)
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Categories: | Blurring filters, Filtering, Image |
Keywords: | convolution, image kernel |
Toolkits: | ITK (moved into the sandbox) |
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![]() by Vercauteren T., Pennec X., Perchant A., Ayache N.
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![]() by Tustison N., Gee J.
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