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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   

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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.sci.utah.edu/cibc/software/map3d.html

A scientific visualization application written to display and edit complex, three-dimensional geometric models and scalar, time-based data associated with those models such as high resolution EEG, MEG, and ECG.

Proper citation: map3d (RRID:SCR_009628) Copy   

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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   

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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   

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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   

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

Software toolbox containing many kinds of kits that you may be interested in during fMRI data analysis. This toolbox is a homebrew kits built during practical ASL(arterial spin labeling) based Cerebral Blood Flow (CBF) data analysis. Meanwhile, this toolbox is also compatible with BOLD data analysis. Everyone would find something useful for their own data analysis! This toolbox is run and tested on SPM8 with MATLAB 7.6.0(R2008a) under the Linux OS. Theoretically, most of the functions (except the menu1&2 which are specially designed for the Batch Editor of SPM8) of this toolbox should be compatible with SPM5 and should also work smoothly under the Windows OS. Feel free to give feedback to authors if you encounter any bugs or problems. Senhua Zhu Center for functional Neuroimaging, University of Pennsylvania 3 W.Gates Bldg, 3400, Philadelphia, PA (19104), United States Email: [email protected] ; [email protected] QQ group number (QQ?): 60524357 Google group: https://groups.google.com/d/forum/fmri-grocer

Proper citation: fMRI Grocer (RRID:SCR_009622) Copy   

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http://elastix.isi.uu.nl/

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on February 23,2023. Software toolbox for rigid and nonrigid registration of images. elastix is open source software, based on the well-known Insight Segmentation and Registration Toolkit (ITK). The software consists of a collection of algorithms that are commonly used to solve (medical) image registration problems. The modular design of elastix allows the user to quickly configure, test, and compare different registration methods for a specific application. A command-line interface enables automated processing of large numbers of data sets, by means of scripting. A paper describing elastix contains more details: S. Klein, M. Staring, K. Murphy, M.A. Viergever, J.P.W. Pluim, elastix: a toolbox for intensity based medical image registration,; IEEE Transactions on Medical Imaging, vol. 29, no. 1, pp. 196 - 205, January 2010., THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

Proper citation: elastix (RRID:SCR_009619) Copy   

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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   

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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

https://www.nitrc.org/projects/w2mhs/

An open source MATLAB toolbox designed for detecting and quantifying White Matter Hyperintensities(WMH) in Alzheimer?s and aging related neurological disorders.Our toolbox provides a self-sufficient set of tools for segmenting these WMHs reliably and further quantifying their burden for down-processing studies. WMHs arise as bright regions on T2-weighted FLAIR images. They reflect comorbid neural injury or cerebral vascular disease burden. Their precise detection is of interest in Alzheimer?s disease (AD) with regard to its prognosis.

Proper citation: Wisconsin White Matter Hyperintensities Segmentation Toolbox (RRID:SCR_009652) 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   

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http://www.unicog.org/pm/pmwiki.php/MEG/RemovingArtifactsWithADJUST

A completely automatic algorithm for artifact identification and removal in EEG data. ADJUST is based on Independent Component Analysis (ICA), a successful but unsupervised method for isolating artifacts from EEG recordings. ADJUST identifies artifacted ICA components by combining stereotyped artifact-specific spatial and temporal features. Features are optimised to capture blinks, eye movements and generic discontinuities. Once artifacted IC are identified, they can be simply removed from the data while leaving the activity due to neural sources almost unaffected.

Proper citation: ADJUST (RRID:SCR_009526) Copy   

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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

http://web.mit.edu/evelina9/www/funcloc.html

Spm-toolbox that performs region of interest (ROI)-level and voxel-level between-subjects analyses of functional MRI data, restricting the analyses to those areas identified using subject-specific functional localizers. Methods: The toolbox implements ROI-level and voxel-level analyses, and it implements an automatic cross-validation procedure when the localizers are not orthogonal to the effects-of-interest. ROI-level analyses allow manually defined parcels of interest, as well as automatically-defined ones (GcSS procedure, Fedorenko et al. 2010). General linear model second-level analyses are implemented, including ReML and OLS estimation of population level effects. Hypothesis testing includes standard univariate tests as well as multivariate tests for mixed within- and between-subject designs (T, F, and Wilks' lambda statistics) This toolbox requires Matlab and SPM5/SPM8.

Proper citation: SPM SS - fMRI functional localizers (RRID:SCR_009644) Copy   

•   Source: SciCrunch Registry

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

They demonstrate and provide R code that can classify between groups of fMRI scans based on functional network connectivity differences, requiring only 4 lines of code to be altered. In addition, they include a detailed article explaining the methods behind and motivations of this tool. This code can also be altered to perform connectivity analysis and classification using ROI based methods by reading in distance arrays previously created. They run Independent component analysis (ICA) on fMRI data to establish functional networks, measure the functional connectivity between these networks using the temporal cross-correlations between independent component to create a distance matrix and indicating the networking. Connectivity properties are used as a feature matrix for an SVM classifier. Collectively, this project provides and explains both methods and code to perform functional network connectivity and fMRI SVM classi?cation.

Proper citation: fMRI Classification in R (RRID:SCR_009519) Copy   

•   Source: SciCrunch Registry

http://www.slicer.org/slicerWiki/index.php/Slicer3:Module:Level-Set_Segmentation_Framework-Documentation

The modules in the framework support different tasks in the segmentation realization in 3DSlicer. A module called Level-set label map evolver was developed, which takes an initial label image and a feature image as input and performs a Geodesic Active Contours evolution on the label image according to the feature image and to a different terms in the level-set equation. The evolution takes place for a customizable number of iterations. The output is a label image that can be used to produce a model. Other modules were developed to accompany the main module as can be seen in http://www.slicer.org/slicerWiki/index.php/Slicer3:Module:Level-Set_Segmentation_Framework-Documentation

Proper citation: Level-set Segmentation for Slicer3 (RRID:SCR_009558) Copy   

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http://www.rad.upenn.edu/sbia/software/dramms/

A software designed for deformable 2D-to-2D and 3D-to-3D image registration. Some typical applications of DRAMMS include, ** Cross-subject registration of the same organ (can be brain, breast, cardiac, etc); ** Mono- and Multi-modality registration (MRI, CT, histology); Longitudinal registration (pediatric brain growth, cancer development, etc); ** Registration under partial missing correspondences (small lesions, tumors, histological cuts). DRAMMS is implemented as a Unix command-line tool. It is fully automatic and easy to use ? users input two images, and DRAMMS will output the registered image and deformation. No need for pre-segmentation of any structures, no need for any prior knowledge, and no need for human initialization or intervention.

Proper citation: DRAMMS (RRID:SCR_009555) Copy   

•   Source: SciCrunch Registry