scholarly journals User Guide to The Computational Morphometry Toolkit

2011 ◽  
Author(s):  
Torsten Rohlfing

This guide is intended as a very brief introduction of the main tools in the Computational Morphometry Toolkit (CMTK), which is available in source code and as precompiled binaries from http://www.nitrc.org/projects/cmtk/. The target audience of this document are CMTK users, who might use this document as a reference to the most common processing tasks, and prospective users, who may find this information useful to determine whether CMTK provides functionality that they can use. We focus in particular on a simplified workflow for deformation morphometry studies based on magnetic resonance images: DICOM conversion, artifact correction, affine and nonlinear image registration, reformatting, Jacobian determinant map generation, and statistical hypothesis testing.

NeuroImage ◽  
2009 ◽  
Vol 44 (3) ◽  
pp. 692-700 ◽  
Author(s):  
Satheesh Maheswaran ◽  
Hervé Barjat ◽  
Simon T. Bate ◽  
Paul Aljabar ◽  
Derek L.G. Hill ◽  
...  

Author(s):  
Sach Mukherjee

A number of important problems in data mining can be usefully addressed within the framework of statistical hypothesis testing. However, while the conventional treatment of statistical significance deals with error probabilities at the level of a single variable, practical data mining tasks tend to involve thousands, if not millions, of variables. This Chapter looks at some of the issues that arise in the application of hypothesis tests to multi-variable data mining problems, and describes two computationally efficient procedures by which these issues can be addressed.


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