Tools   Select Another Resource Report Type

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

Quadratically constrained quadratic programing (QCQP) technique in medical image analysis. QCQP based tools are provided for classification, segmentation, and bias field correction.

Proper citation: QCQP (RRID:SCR_009640) Copy   

•   Source: SciCrunch Registry

http://www.columbia.edu/~dx2103/brainimagescope.html

Software package for processing diffusion tensor imaging data. The following functions are included: 1. Converting imaging data in DICOME format to ANALYZE format 2. Extracting binary brain mask for quick scalp-removing 3. Correcting eddy-current induced distortion 4. Optimized tensor estimation based on noisy diffusion-weighted imaging (DWI) data 5. Scalp removal using a brain mask image 6. Corregistering imaging data and generating deformation field for mapping images from individual spaces to a template or target space 7. Spatial Normalization and Warping DTI 8. Fiber tracking 9. Clustering fiber tracts 10. Identifying brain ventricles and generating binary masks for the baseline and DW imaging data 11. Deriving diffusion anisotropy indices (DAIs) and principal directions (PD) and the corresponding color-coded PD-map.

Proper citation: DTI BrainImageScope (RRID:SCR_009559) Copy   

•   Source: SciCrunch Registry

http://beast.bio.ed.ac.uk/

A cross-platform software program for Bayesian MCMC analysis of molecular sequences. It is entirely orientated towards rooted, time-measured phylogenies inferred using strict or relaxed molecular clock models. It can be used as a method of reconstructing phylogenies but is also a framework for testing evolutionary hypotheses without conditioning on a single tree topology. BEAST uses MCMC to average over tree space, so that each tree is weighted proportional to its posterior probability. We include a simple to use user-interface program for setting up standard analyses and a suit of programs for analysing the results.

Proper citation: BEAST (RRID:SCR_010228) Copy   

•   Source: SciCrunch Registry

http://cocomac.org/WWW/paxinos3D/index.html

An interactive interface of macaque stereotaxic atlas with a connectivity database, allowing integrated data analysis and mapping between 3D structures with database vocabularies. These Java-based tools are capable of reading stacks of polygons described in svg vector format and arrange them in 3D space so that the corresponding structures can be viewed and manipulated individually. An additional excel (currently v. 1997-2003) file maintains the structure abbreviations and their mapping to the terminology of databases that provide supplementary information. Here in particular we have manually drawn the cortical, striatal, thalamic and amygdaloid structures of the 151 frontal sections from the Rhesus Monkey Brain in Stereotactic Coordinates authored by Paxinos and colleagues in 1999. After loading the excel file and a set of the svg files, the view can be rotated, zoomed and individual brain structures be selected for identification and simple geometric measures. A stereotaxic grid is a display option. The abbreviations of the brain structures are mapped to entities recorded in the CoCoMac database of primate brain connectivity. Thereby one can retrieve mapping and connectivity information for the selected structure as text or connecting arrows.

Proper citation: CoCoMac-Paxinos3D viewer (RRID:SCR_009548) Copy   

•   Source: SciCrunch Registry

http://invizian.loni.usc.edu

A visualization environment that enables you, via your computer, to display and interact with hundreds of neuroimaging data sets at once ?bringing together brain image data from some of the world?s best neuroscience research teams. INVIZIAN empowers both researchers and students of neuroscience to explore and understand the human brain using a simple yet powerful user interface for neuroimaging data exploration and discovery. See a beautiful example of a cloud of individual brains tumbling around in the INVIZIAN interface in Vimeo (http://vimeo.com/67984681). Visit often to see how we are making continuing progress to make Invizian even more amazing.

Proper citation: INVIZIAN (RRID:SCR_009549) Copy   

•   Source: SciCrunch Registry

http://omlc.ogi.edu/software/mc/

MCML is a Monte Carlo simulation program for Multi-layered Turbid Media with an infinitely narrow photon beam as the light source. The simulation is specified by an input text file called, for example, sample.mci, which can be modified by any simple text editor. The output is another text file called, for example, sample.mco. (The names are arbitrary.) CONV is a convolution program which uses the MCML output file to convolve for photon beams of any size in a Gaussian or flat field shape. CONV can provide a variety of output formats (reflectance, transmission, iso-fluence contours, etc.), which are compatible with standard graphics applications.

Proper citation: MCML and CONV (RRID:SCR_002419) Copy   

•   Source: SciCrunch Registry

http://www.thevirtualbrain.org/

Simulation software for modeling the entire human brain by combining structural and functional data from empirical neuroimaging data. It can generate local field potentials, EEG, MEG and fMRI BOLD data based on neural mass models. The user can also modify the model parameters to match clinical conditions from focal lesions or degenerative disorders.

Proper citation: Virtual brain (RRID:SCR_002249) Copy   

•   Source: SciCrunch Registry

http://mgui.wikidot.com

An open source Java-based project intended to provide a graphic user interface (GUI) for interactions between scientists (or enthusiasts) and their data. In its current (beta) form, mgui offers the following functionality: * Cross-platform functionality (with a Java Runtime installation, runs on Linux, Windows, Mac, or Solaris) * 2D rendering of data based upon Java2D, and 3D rendering based upon Java3D * The ability to organize complex datasets into intuitive mgui projects * A processing pipeline interface which allows users to process their datasets with any available Java or native software tools * An extensible I/O framework accommodating a variety of standard and non-standard file formats * Database connectivity using JDBC * Graph visualization based upon the JUNG library * An intuitive Swing-based GUI for managing, querying, and visualizing data * Various CAD-type tools for editing and creating geometry * A computational modelling framework

Proper citation: ModelGUI (RRID:SCR_002441) Copy   

•   Source: SciCrunch Registry

http://air.bmap.ucla.edu/MultiTracer2/MultiTracer.html

A Java application that allows images to be displayed in three dimensions. The tool allows anatomic structures to be traced and the tracings to be saved in a format that facilitates review and revision. It supports NIfTI-1.1 format float, double and signed and unsigned byte, short, and integer formats and provides legacy support for Analyze 7.5 8 and 16 bit images. It provides image display, editing, delineation of structure boundaries, export of traced contours and generation of masked volumes. Images are displayed in 3 orthogonal views. Time series can be displayed as averaged or contrast images and time courses can be visualized graphically. Version 2 provides enhancements to the original MultiTracer feature set.

Proper citation: MultiTracer (RRID:SCR_002445) Copy   

•   Source: SciCrunch Registry

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

Software package that is a powerful graphical interface that displays, segments, aligns, manipulates, and blends image (pixel) and geometry (real-world coordinates) data simultaneously. Several applications are directly built into MIVA. Registration modes include interactive affine transformations. Fiducial registration tools facilitate rapid alignments for inter-modality volumes. Interactive Region of Interst (ROI) and Volume-of-Interest (VOI) tools exist to segment medical images. Virtually unique to MIVA are its 3D geometry tools and their compatibility with pixel based medical images. A full 3D interactive rat brain atlas is in an fMRI module which walks one through the necessary steps of fMRI. A multiple material surface routine takes segmented medical slices and creates 3D triangulated surfaces that align along all region boarders without overlap or gaps. These surfaces are the direct input into the MIVA tetrahedral mesh generator.

Proper citation: Medical Image Visualization and Analysis (RRID:SCR_002315) Copy   

•   Source: SciCrunch Registry

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

This simple visualization tool allows to load several images at the same time. The cursor across all windows are coupled and you can move/zoom on all the images at the same time. Very useful for quality control, image comparison.

Proper citation: MriWatcher (RRID:SCR_002318) Copy   

•   Source: SciCrunch Registry

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

An efficient level set framework for multi-object segmentation. Its representation inherently prevents overlaps and gaps and it readily preserves object topology and object relationships. MGDM is efficient, storing only a fixed number of functions for any number of objects, and therefore scales well to segmentation problems with many classes and large images. It's representation also avoids some instabilities in other multi-class level set methods. MGDM is cross-platform; MATLAB wrappers, Java source and API are provided, with MIPAV plugins forthcoming.

Proper citation: MGDM: Multi Geometric Deformable Model (RRID:SCR_002311) Copy   

•   Source: SciCrunch Registry

http://www.med.unc.edu/bric/ideagroup/free-softwares/hammer

Software for both groupwise registration and longitudinal registration, which are the necessary steps for many brain-related applications. Specifically, groupwise registration is important for unbiased analysis of a large set of MR brain images. Therefore, in this software package, they have included two of their recently-developed groupwise registration algorithms: 1) Improved unbiased groupwise registration guided with the sharp group-mean image, and 2) Hierarchical feature-based groupwise registration with implicit template (Groupwise-HAMMER for short). On the other hand, they also included their recently-developed groupwise longitudinal registration algorithm that aligns not only the longitudinal image sequence for each subject, but also align all longitudinal image sequences of all subjects to the common space simultaneously.

Proper citation: GLIRT (RRID:SCR_002390) Copy   

•   Source: SciCrunch Registry

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

Training material for the MS lesion segmentation challenge 2008 to compare different algorithms to segment the MS lesions from brain MRI scans. Data used for the workshop is composed of 54 brain MRI images and represents a range of patients and pathology which was acquired from Children's Hospital Boston and University of North Carolian. Data has initially been randomized into three groups: 20 training MRI images, 24 testing images for the qualifying and 8 for the onsite contest at the 2008 workshop. The downloadable online database consists now of the training images (including reference segmentations) and all the 32 combined testing images (without segmentations). The naming has not been changed in comparison to the workshop compeition in order to allow easy comparison between the workshop papers and the online database papers. One dataset has been removed (UNC_test1_Case02) due to considerable motion present only in its T2 image (without motion artifacts in T1 and FLAIR). Such a dataset unfairly penalizes methods that use T2 images versus methods that don't use the T2 image. Currently all cases have been segmented by expert raters at each institution. They have significant intersite variablility in segmentation. MS lesion MRI image data for this competition was acquired seperately by Children's Hospital Boston and University of North Carolina. UNC cases were acquired on Siemens 3T Allegra MRI scanner with slice thickness of 1mm and in-plane resolution of 0.5mm. To ease the segmentation process all data has been rigidly registered to a common reference frame and resliced to isotrophic voxel spacing using b-spline based interpolation. Pre-processed data is stored in NRRD format containing an ASCII readable header and a separate uncompressed raw image data file. This format is ITK compatible. If you want to join the competition, you can download data set from links here, and submit your segmentation results at http://www.ia.unc.edu/MSseg after registering your team. They require team name, password, and email address for future contact. Once experiment is completed, you can submit the segmentation data in a zip file format. Please refer submission page for uploading data format.

Proper citation: MS lesion segmentation challenge 2008 (RRID:SCR_002425) Copy   

•   Source: SciCrunch Registry

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

A program for registering images with with mutual information based metric. Several registration options are given for 3,6, 9,12,16 parameter (i.e. translate, rigid, scale, scale/skew, full affine) based constraints for the registration. The program uses the Slicer3 execution model framework to define the command line arguments and can be fully integrated with Slicer3 using the module discovery capabilities of Slicer3

Proper citation: BRAINSFit (RRID:SCR_002340) Copy   

•   Source: SciCrunch Registry

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

An advanced viewer for electrophysiological and behavioral data: it can display local field potentials (EEG), neuronal spikes, behavioral events, as well as the position of the animal in the environment. It also features limited editing capabilities.

Proper citation: NeuroScope (RRID:SCR_002455) Copy   

•   Source: SciCrunch Registry

http://www.bic.mni.mcgill.ca/software/N3/

The perl script nu_correct implements a novel approach to correcting for intensity non-uniformity in MR data that achieves high performance without requiring supervision. By making relatively few assumptions about the data, the method can be applied at an early stage in an automated data analysis, before a tissue intensity or geometric model is available. Described as Non-parametric Non-uniform intensity Normalization (N3), the method is independent of pulse sequence and insensitive to pathological data that might otherwise violate model assumptions. To eliminate the dependence of the field estimate on anatomy, an iterative approach is employed to estimate both the multiplicative bias field and the distribution of the true tissue intensities. Preprocessing of MR data using N3 has been shown to substantially improve the accuracy of anatomical analysis techniques such as tissue classification and cortical surface extraction.

Proper citation: MNI N3 (RRID:SCR_002484) Copy   

•   Source: SciCrunch Registry

http://www.med.unc.edu/bric/ideagroup/free-softwares/libra-longitudinal-infant-brain-processing-package

A toolbox with graphical user interfaces for processing infant brain MR images. Longitudinal (or single-time-point) multimodality (including T1, T2, and FA) (or single-modality) data can be processed using the toolbox. Main functions of the software (step by step) include image preprocessing, brain extraction, tissue segmentation and brain labeling. Linux operating system (64 bit) is required. A workstation or server with memory >8G is recommended for processing many images simutaneously. The graphical user interfaces and overall framework of the software are implemented in MATLAB. The image processing functions are implemented with the combination of C/C++, MATLAB, Perl and Shell languages. Parallelization technologies are used in the software to speed up image processing.

Proper citation: iBEAT (RRID:SCR_002470) Copy   

•   Source: SciCrunch Registry

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

Software to fit s-reps to segmented anatomic objects, to compute probability distributions on these s-reps, to train and to apply classifiers between two classes of anatomic objects, and to apply hypothesis testing to determine which geometric or physiological features vary significantly between two classes. Software for object segmentation from medical images may also be included. S-reps are skeletal models for anatomic objects especially suited for computing probability distributions from populations of these objects and for providing object-related coordinates for the interior of these objects. They allow classification and hypothesis testing using their geometric features and physiological features derived from medical images. They also allow the definition of shape spaces, probability-based geometric typicality functions, and appearance models used for segmentation or registration. A variety of successful applications to objects in neuroimages have already been performed.

Proper citation: S-rep Fitting Statistics and Segmentation (RRID:SCR_002540) Copy   

•   Source: SciCrunch Registry

http://scralyze.sourceforge.net

A powerful software for model-based analysis of peripheral psychophysiology (e.g. skin conductance, heart rate, pupil size etc.). General linear modelling and dynamic causal modelling of these signals provide for inference on neural states/processes. SCRalyze includes flexible data import and display, statistical inference and results display and export. Easy programming of add-ons for new data formats, signal channels, and models.

Proper citation: SCRalyze (RRID:SCR_002542) Copy   

•   Source: SciCrunch Registry