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A C# DICOM Library
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A simple to use C# library for reading and manipulating DICOM files. New documentation added to github via Github pages.

  • Online API via DocFX
  • Dot Net Standard Compliant (multiplatform)
  • MIT Open Source license
  • Examples On GH Pages
  • NuGet packages released with each build
  • 10,000+ downloads

Wickedly Simple

var dcm = DICOMObject.Read("MyDICOMFile.dcm");
//***COOL CODE GOES HERE***
//Writing is equally easy
dcm.Write("MyHackedDICOMFile.dcm");

Read more at the project website at http://rexcardan.github.io/Evil-DICOM/

Content Link
Introductory Video Youtube
Examples Example Operations
Online API API Documentation

Evil-DICOM used and featured in publications

  1. Mayo, C., et al., Demonstration of a software design and statistical analysis methodology with application to patient outcomes data sets. Medical physics, 2013. 40(11): p. 111718.

  2. Vickress, J., R. Barnett, and S. Yartsev, Data Inventory for Cancer Patients Receiving Radiotherapy for Outcome Analysis and Modeling. Biomedical Data Mining, 2014. 3(105): p. 2.

  3. Saalfeld, P., et al., Touchless measurement of medical image data for interventional support. Mensch und Computer 2017-Tagungsband, 2017.

  4. Miras, H., et al., Monte Carlo verification of radiotherapy treatments with CloudMC. Radiation Oncology, 2018. 13(1): p. 99.

  5. Patrick Saalfeld, D.K. and C.H. Bernhard Preim, Image Data for Interventional Support. Mensch und Computer 2017-Tagungsband: Spielend einfach interagieren, 2018. 17: p. 83.

  6. Pyyry, E.J. and W. Keranen, Varian APIs: A handbook for programming in the Varian oncology software ecosystem. 2018.

  7. Saalfeld, P., J. Patzschke, and B. Preim, An immersive system for exploring and measuring medical image data. Mensch und Computer 2017-Tagungsband: Spielend einfach interagieren, 2018. 17: p. 73.

  8. Alkhimova, S. and V. Kuleshov, Analysis of turning angle in scope of brain tissue segmentation with CUSUM filter. 2019.

  9. Alkhimova, S. and S. Sliusar, Analysis of effectiveness of thresholding in perfusion ROI detection on T2-weighted MR images with abnormal brain anatomy. arXiv preprint arXiv:1912.05469, 2019.


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