Dinifti
The dinifti program converts MRI images stored in DICOM format to NIfTI format.
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The dinifti program converts MRI images stored in DICOM format to NIfTI format.
Niftilib is a set of i/o libraries for reading and writing files in the nifti-1 data format. nifti-1 is a binary file format for storing medical image data, e.g. magnetic resonance image (MRI) and functional MRI (fMRI) brain images.
Niftilib currently has C, Java, MATLAB, and Python libraries; we plan to add some MATLAB/mex interfaces to the C library in the not too distant future.
The project stands for Medical Image Conversion. Released under the (L)GPL licence, it comes with the full C-source code of the library, a flexible command-line utility and a neat graphical front-end using the Gtk+ toolkit. The supported formats are: Acr/Nema 2.0, Analyze (SPM), Concorde/µPET, DICOM 3.0, CTI ECAT 6/7, NIfTI-1, InterFile3.3 and PNG or Gif87a/89a.
DataViewer3D (DV3D) is a multi-modal imaging data visualization tool offering a cross-platform, open-source solution to simultaneous data overlay visualization requirements of imaging studies.
Brainstorm is a collaborative open-source Matlab application dedicated to magnetoencephalography (MEG) and electroencephalography(EEG) data visualization, processing and cortical source estimation.
The intention is to make a comprehensive set of tools available to the scientific community involved in MEG/EEG experimental research.
For physicians and researchers, the interest of this software package resides in its rich and intuitive graphic interface, which does not require any programming knowledge.
Ogles2 is an interactive slice and volume visualization and analysis tool based on Open Inventor / Coin3D. Ogles2 allows for reproducing the workflow of frame based stereotactic neurosurgery. In the long run it strives for being an open source stereotactic planning and analysis system. Ogles2 is NOT APPROVED FOR CLINICAL USE.
ITK-SNAP is a software application used to segment structures in 3D medical images. It is the product of a decade-long collaboration between Paul Yushkevich, Ph.D., of the Penn Image Computing and Science Laboratory (PICSL)
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