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Software application which aims to assign metric distances on the space of anatomical images in Computational Anatomy thereby allowing for the direct comparison and quantization of morphometric changes in shapes. As part of these efforts the Center for Imaging Science at Johns Hopkins University developed techniques to not only compare images, but also to visualize the changes and differences. For additional information please refer to: Faisal Beg, Michael Miller, Alain Trouve, and Laurent Younes. Computing Large Deformation Metric Mappings via Geodesic Flows of Diffeomorphisms. International Journal of Computer Vision, Volume 61, Issue 2; February 2005. M.I. Miller and A. Trouve and L. Younes, On the Metrics and Euler-Lagrange Equations of Computational Anatomy, Annual Review of biomedical Engineering, 4:375-405, 2002. Software developed with support from National Institutes of Health NCRR grant P41 RR15241.
Proper citation: LDDMM (RRID:SCR_009590) Copy
http://www.nitrc.org/projects/caworks
A software application developed to support computational anatomy and shape analysis. The capabilities of CAWorks include: interactive landmark placement to create segmentation (mask) of desired region of interest; specialized landmark placement plugins for subcortical structures such as hippocampus and amygdala; support for multiple Medical Imaging data formats, such as Nifti, Analyze, Freesurfer, DICOM and landmark data; Quadra Planar view visualization; and shape analysis plugin modules, such as Large Deformation Diffeomorphic Metric Mapping (LDDMM). Specific plugins are available for landmark placement of the hippocampus, amygdala and entorhinal cortex regions, as well as a browser plugin module for the Extensible Neuroimaging Archive Toolkit.
Proper citation: CAWorks (RRID:SCR_014185) Copy
http://www.nitrc.org/projects/pca-scalar-mesh
An implementation of standard PCA algorithms for use on scalar or vector data sets. Kernel PCA is implemented in this class, where the data sets are scalar or vector valued functions assigned at each of the points in a PointSet. A Gaussian Distance Kernel class is provided with the PCA class.
Proper citation: Principal Components Analysis of Scalar, Vector, and Mesh Vertex Data (RRID:SCR_014163) Copy
http://www.nitrc.org/projects/jist/
A native Java-based imaging processing environment similar to the ITK/VTK paradigm. Initially developed as an extension to MIPAV (CIT, NIH, Bethesda, MD), the JIST processing infrastructure provides automated GUI generation for application plug-ins, graphical layout tools, and command line interfaces. This repository maintains the current multi-institutional JIST development tree and is recommended for public use and extension. JIST was originally developed at IACL and MedIC (Johns Hopkins University) and is now also supported by MASI (Vanderbilt University).
Proper citation: JIST: Java Image Science Toolkit (RRID:SCR_008887) Copy
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