Multilocal Creaseness Measure
Alma IT Systems
| Please use this identifier to cite or link to this publication: http://hdl.handle.net/10380/3338 |
Published in The Insight Journal - 2012 January-June.
Submitted by Sergio Vera on 01-10-2012.
This document describes the implementation using the Insight Toolkit of an algorithm for detecting creases (ridges and valleys) in N-dimensional images, based on the Local Structure Tensor of the image. In addition to the filter used to calculate the creaseness image, a filter for the computation of the structure tensor is also included in this submission.
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Useful structure tensor and creasness measure implementation
by Dženan Zukić on 2012-01-16 08:42:23 for revision #1 
expertise: 3 sensitivity: 5 
Hypothesis:
Creasness measure implementation based on structure tensor.
Evidence:The authors describe the principles in short, and give further references.
Open Science:Source code and one image provided, along with reference output image.
Reproducibility:The code needed minor modification to be compilable with ITK v4. The provided example runs.
Open source Contributions:Code is in the form of reusable templated filters, the standard ITK style.
Code Quality :The code does not have many comments, but seems to be using standard style.
Free comment :A useful contribution that will save someone a few weeks that it takes to implement it.
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| Categories: | Derivatives and Integrals, Feature extraction, Higher order derivatives |
| Keywords: | Ridges, Valley, Creaseness, Structure Tensor, Skeleton, |
| Toolkit: | ITK |
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