Search Results for "java image segmentation"
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Showing 6 open source projects for "java image segmentation"
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The MITK Workbench is a free, open-source application for medical image visualization, segmentation, registration, and much more. Beyond the Workbench application, MITK is a comprehensive C++ framework for medical image computing. It provides a modular foundation for extending the MITK Workbench with custom plugins or developing your own medical imaging applications and research prototypes.
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InVesalius
3D medical imaging reconstruction software
InVesalius is a free, open-source medical imaging application designed for three-dimensional reconstruction from computed tomography and magnetic resonance imaging scans. It converts sequences of two-dimensional DICOM images into detailed anatomical models. The software supports medical image visualization, segmentation, and measurement through an interactive graphical environment. Users can isolate tissues using threshold ranges, watershed segmentation, and manual editing tools. It provides high-quality volume rendering and three-dimensional surface generation for examining anatomical structures. Reconstructed models can be exported in formats such as STL, OBJ, and PLY for further processing or 3D printing. ... -
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XMIPP is an image processing suite for 3D-reconstruction of biological specimens from transmission electron microscopy data. NOTE that stable releases (including binaries) are longer stored at Sourceforge, but are available at http://xmipp.cnb.csic.es
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BioImageXD - free open source software for analysis, processing and 3D rendering of multi dimensional, multi data channel, time series image data from microscopy and other sources.
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Medical Image Computing Workflow (MICFlow) is a simple, easy but flexible, extensible and powerful workflow system to automate medical image computing tasks such as segmentation, registration and analysis. It's pure Python so can be used on any platform
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The 3D Slicer is freely available, open-source software for visualization, registration, segmentation, and quantification of medical image data. Follow the link for more info: http://slicer.org
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