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http://www.loni.usc.edu/Software/BrainParser

Software that uses a novel statistical-learning technique to segment brain regions of interest (ROIs) based on a training set of data and generates 3D MRI volumes. The software comes pre-trained on a provided data set but can be retrained to work with your desired regions of interest.

Proper citation: LONI Brain Parser (RRID:SCR_009572) Copy   

•   Source: SciCrunch Registry

http://www.nitrc.org/projects/c8c8/

A small, stand-alone MatLab toolbox that measures sagittal cross-section thickness and area of the human corpus callosum from high-resolution T1 in vivo MR images. C8 takes as input affine normalized white matter segmentations derived from high-resolution (in-plane) T1 images and outputs both regional callosal thicknesses in three different formats and geometrically-defined regional areas in three different configurations. It is a small package that is easily configurable and modifiable and it measures callosa at the rate of several per minute.

Proper citation: C8: Corpus Callosum Computations (RRID:SCR_009449) Copy   

•   Source: SciCrunch Registry

http://www.nitrc.org/projects/best_practices/

A tutorial that took place as part of MICCAI 2010 is the 13th International Conference on Medical Image Computing and Computer Assisted Intervention, September 20-24, 2010 in Beijing, China. See http://www.miccai2010.org/ This project supporedt community outreach and dialog between presenters and audience, both before and after the tutorial session, and is intended to engage the broader community in the deliberations on this important topic. ''Best Practices'' covered software engineering practices as well end-user installation and support practices.

Proper citation: Best Practices for Software Development (RRID:SCR_009441) Copy   

•   Source: SciCrunch Registry

http://www.nitrc.org/projects/btk/

Software toolkit developed for the fbrain project that consists of several image processing tools: image reconstruction, image denoising, image segmentation, tractography etc., for a better understanding of fetal brain development.

Proper citation: Baby Brain Toolkit (RRID:SCR_009440) Copy   

•   Source: SciCrunch Registry

http://www.slicer.org/slicerWiki/index.php/Documentation/Nightly/Extensions/DTIProcess

A DTI processing and analysis toolkit developed in UNC and University of Utah. Tools in this toolkit include dtiestim, dtiprocess, dtiaverage, fibertrack, fiberprocess, et al..

Proper citation: DTIProcess ToolKit (RRID:SCR_009561) Copy   

•   Source: SciCrunch Registry

http://www.nitrc.org/projects/fopa/

A standardized framework for communication and data exchange between medical imaging applications, with particular focus on neuroimaging technologies. FOPA is an attempt to design and implement a common protocol for network and command line communication with either file-system or imbedded data structures. Initial reference implementations will support interoperability between ITK, VTK, and Java platforms. Contributions are welcome from other neuroimaging development communities.

Proper citation: Framework for Open Programmatic Access (RRID:SCR_009479) Copy   

•   Source: SciCrunch Registry

http://www.nitrc.org/projects/groupwisereg/

An open source implementation of a non-rigid groupwise registration method. This project is implemented by Serdar K Balci (serdar at csail.mit.edu) and supervised by Polina Golland and William M. Wells All metrics are implementing in a multi-threaded fashion. The algorithm will run faster on computers with multiple CPU''s.

Proper citation: Non-rigid groupwise registration method (RRID:SCR_009512) Copy   

•   Source: SciCrunch Registry

http://www.nitrc.org/projects/books

Bibliography of books related to neuroscience addressing the topic of functional and structural neuroimaging.

Proper citation: NITRC Books (RRID:SCR_009510) Copy   

•   Source: SciCrunch Registry

http://www.nitrc.org/projects/ern/

An efficient framework for building and analyzing graphs called epsilon radial networks (ERNs) using tractography data in a normalized space. Currently there is no agreed-upon method for constructing the brain anatomical connectivity graphs out of large number of white matter tracts. The key challenge in defining brain networks is node delineation and their method defines nodes in the graph using tract-end points clustered in a sphere of a given radius (epsilon). Using a kd-tree based search algorithm they can identify the nodes computationally efficiently and in a fully automatic way. These networks can be used not only to analyze topo-physical properties of the structural brain networks but also to perform classical region-of-interest (ROI) analyses in a very efficient way. Thus ERNs can be used as a novel image processing lens for statistical and machine learning based analyses.

Proper citation: Epsilon Radial Networks (RRID:SCR_009470) Copy   

•   Source: SciCrunch Registry

http://libeep.sourceforge.net/

Software library that deals with reading and writing RIFF-format CNT/AVR-files. This file format is also called EEProbe data format, and is used in the software packages EEProbe, ASA, ASA-Lab, Cognitrace, eemagine EEG, Visor, by ANT Neuro B.V., The Netherlands. The file format provides for storage of EEG/ERP/MEG data as 32-bit values, and includes a very efficient compression algorithm. Encoding/decoding from the compressed data is performed automatically through the LIBEEP interface functions.

Proper citation: LIBEEP (RRID:SCR_009591) Copy   

•   Source: SciCrunch Registry

http://www.nitrc.org/projects/lll/

Software using a novel local label learning strategy to estimate the target image?s segmentation label using statistical machine learning techniques. They used a support vector machine (SVM) with a K nearest neighbor (KNN) based training sample selection strategy to learn a classifier for each of the target image voxel based on a training dataset consisting of its neighboring voxels in the atlases. Validation experiments on hippocampus segmentation of 117 MR images demonstrated that the method can produce segmentation results consistently better than state-of-the-art label fusion methods.

Proper citation: Local Label Learning Segmentation (RRID:SCR_009504) Copy   

•   Source: SciCrunch Registry

http://www.nitrc.org/projects/landman/

Project to provide long-term hosting and release for small tools related to medical image analysis. Source repository contains highly experimental code intended for collaborative development. However, any interested parties are welcome to browse/reuse code. Stable/evolved projects will be moved to independent projects.

Proper citation: Landman NeuroImaging Tools (RRID:SCR_009503) Copy   

•   Source: SciCrunch Registry

http://www.nitrc.org/projects/jhumipavplugins/

This repository stores plugins, tutorial code, and examples demonstrating MRI manipulation within the MIPAV plugin environment. This project is separate from JIST so that we can provide WRITE access to any interested party without overly exposing the infrastructure to unplanned modification. Please contact the administrators if you would like to join this project - open use is encouraged.

Proper citation: JIST Resources for Algorithm Development (RRID:SCR_009500) Copy   

•   Source: SciCrunch Registry

http://www.nitrc.org/projects/dkfz-diffusion/

This central project points to all open-source and open-data initiatives provided by the German Cancer Research Center in the field of diffusion MRI.

Proper citation: Diffusion MRI at DKFZ Heidelberg (RRID:SCR_009465) Copy   

•   Source: SciCrunch Registry

http://www.nitrc.org/projects/diffusionwarp/

Software tools appropriate for the registration of diffusion tensor images to an average coordinate system. The tools include image registration methods and algorithms for the correct alignment of the diffusion tensor when applying the resulting transformation. The program uses the Slicer3 execution model framework to define the command line arguments, and can be fully integrated using the module discovery capabilities of Slicer3.

Proper citation: Diffusion Warp (RRID:SCR_009466) Copy   

•   Source: SciCrunch Registry

http://www.nitrc.org/projects/brainmask/

Segmentation of the brain from three-dimensional MR images is a crucial pre-processing step in morphological and volumetric brain studies. BrainMask implements a fully automatic brain segmentation algorithm that uses advanced thresholding with morphology and 3D edge detection algorithms. BrainMask demonstrates high segmentation accuracy. For a representative 26 datasets, the segmentation error averaged 3.4% ������ 1.3% (Mikheev A et al. J Magn Reson Imag 27(6):1235-41;2008). BrainMask includes NNN - a tool based on the algorithm developed by John Sled for correcting the intensity non-uniformity in MR data (Sled JG et al. IEEE Trans Med Imag 17(1):87-97;1998). BrainMask also includes a versatile DICOM wiewer and allows to selectively load and organize DICOM images into 3D and 4D datasets.

Proper citation: BrainMask Volume Processing Tool (RRID:SCR_009538) Copy   

•   Source: SciCrunch Registry

http://brainbrowser.cbrain.mcgill.ca

A web-enabled brain surface viewer that allows the user to explore in real time a 3D brain map expressed on a base surface. BrainBrowser has two modes of operation, exploring either a pre-calculated database of structural correlation maps or working with user-defined data. In this mode, the user may choose to explore the correlation structure for cortical thickness, cortical area or cortical volume, or any other pre-calculated metric. In the second mode, the user is prompted for the local filenames of the statistical map and the base surface. BrainBrowser can also be used to manipulate 3D fibre pathways derived from DTI, using the same simple file format (.obj) as for surface data. BrainBrowser on Youtube: http://www.youtube.com/watch?v=HlRTUYUf1Ew NOTE: BrainBrowser requires a WebGL-enabled browser such as Google Chrome to support its 3D graphics capability.

Proper citation: BrainBrowser (RRID:SCR_009535) Copy   

•   Source: SciCrunch Registry

http://support.brainvoyager.com/available-tools/52-matlab-tools-bvxqtools.html

A Matlab-based toolbox for the reading, writing, and processing of BrainVoyager (QX) files in Matlab. The toolbox is freely available.

Proper citation: BVQXtools (RRID:SCR_009532) Copy   

•   Source: SciCrunch Registry

http://www.birncommunity.org/tools-catalog/b0-and-eddy-current-correction-code-for-diffusion-mri/

Software tool (excecutable and source code in C and C++) to correct distortions in diffusion MR images that are generated by main magnetic field inhomogeneities and eddy current induced fields generated from the direction-dependent diffusion encoding

Proper citation: B0 and eddy current correction for DTI (RRID:SCR_009529) Copy   

•   Source: SciCrunch Registry

https://github.com/BRAINSia/BRAINSTools/tree/master/BRAINSDemonWarp

A command line program for image registration by using different methods including Thirion and diffeomorphic demons algorithms. The function takes in a template image and a target image along with other optional parameters and registers the template image onto the target image. The resultant deformation fields and metric values can be written to a file. The program uses the Insight Toolkit (www.ITK.org) for all the computations, and can operate on any of the image types supported by that library. This a an ITK based implementation of various forms of Thirion Demons based registration (including diffeomorphic demons registration originating from Tom Vercauteren at INRIA ).

Proper citation: BRAINSDemonWarp (RRID:SCR_009524) Copy   

•   Source: SciCrunch Registry