An ITK-based Implementation of the Stochastic Rank Correlation (SRC) Metric
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Please use this identifier to cite or link to this publication: http://hdl.handle.net/10380/3229
Recently, Birkfellner et al. proposed a novel image-to-image merit function (stochastic rank correlation, SRC) for robust intensity-based 2D/3D image registration. In this work, we summarize the basic idea of SRC, and present a generic ITK-based implementation of this image-to-image metric including tests for software verification. Moreover, we provide two simple examples that demonstrate the usage of this metric: a) within the native ITK 2D/3D image registration method, and b) within a recently published extended ITK-based 2D/3D registration framework. It is, however, important to note, that this paper neither covers a comprehensive evaluation of SRC, nor a comparison with other metrics. It rather shows that SRC appears to succeed on a femoral and a porcine data set in the course of ITK-based 2D/3D image registration.
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plus Automatic Testing Results by Insight-Journal Dashboard on Wed Jan 19 18:22:28 2011 for revision #3
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plus Automatic Testing Results by Insight-Journal Dashboard on Mon Nov 22 15:05:48 2010 for revision #2
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plus Automatic Testing Results by Insight-Journal Dashboard on Mon Nov 22 09:58:44 2010 for revision #1
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plus Very valuable contribution to ITK by Karl Fritscher on 2011-02-09 02:28:30 for revision #3
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Comment by Sebastian Sauppe yellow
A lot of errors during building with Visual Studio 2010... Doesn't work at all :(


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Categories: Filtering, Registration, Registration metrics
Keywords: metric, 2d/3d image registration, intensity-based, stochastic rank correlation, SRC, ITK
Toolkits: CMake, ITK, CMake, ITK
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