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The 'epi' R Package

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The Epi package is mainly focused on "classical" chronic disease epidemiology. The package has grown out of the course Statistical Practice in Epidemiology using R.


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3D medical image platform for visualization and image processing. Segmentation with Levels sets. Surface reconstruction with marching Cubes, texture Mapping and Raycasting, DICOM support.


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pydicom is a pure python package for working with DICOM files. It was made for inspecting and modifying DICOM data in an easy "pythonic" way. The modifications can be written again to a new file. As a pure python package, it should run anywhere python runs without any other requirements.

pydicom is not a DICOM server, and is not primarily about viewing images. It is designed to let you manipulate data elements in DICOM files with python code.


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DicomBrowser is an application for inspecting and modifying DICOM metadata in many files at once. A single imaging session can produce thousands of DICOM files; DicomBrowser allows users to view and edit a whole session—or even multiple sessions—at once. Users can save the original or modified files to disk, or send them across a network to a DICOM C-STORE service class provider, such as a PACS or an XNAT.

The 'epitools' R Package

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"epitools (epidemiology tools) is an R package for epidemiologic computing and graphics."


Your rating: None Average: 3.6 (37 votes)

ClearCanvas Workstation is our friendly, integrated RIS Client and DICOM PACS viewer. Because it is built on top of our highly extensible application framework, we expect that it will be appropriate not just for radiologists and clinicians, but also researchers who want to build new, cutting edge tools that can be easily "tried out" in a clinical environment. Like our other creations, ClearCanvas Worksation is free and open source.

Feature Highlights

  • Very easy to use, intuitive interface
  • Integration with ClearCanvas RIS


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"DataServer is an open source XML gateway, specially tailored for the medical domain. DataServer is middleware, situated between clients and traditional Health Information Systems (HIS), Radiology Information Systems (RIS) and Picture Archive and Communication Systems (PACS). It supports relational (SQL), SOAP, and HTTP data sources out of the box, but is highly extensible for custom types."


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"PyMVPA is a Python module intended to ease pattern classification analyses of large datasets. In the neuroimaging contexts such analysis techniques are also known as decoding or MVPA analysis. PyMVPA provides high-level abstraction of typical processing steps and a number of implementations of some popular algorithms. While it is not limited to the neuroimaging domain, it is eminently suited for such datasets. PyMVPA is truly free software (in every respect) and additionally requires nothing but free-software to run."

Medical Exploration Toolkit (METK)

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The MedicalExplorationToolkit (METK) was designed for loading, visualizing and exploring segmented medical data sets. It is a framework of several modules in MeVisLab, a development environment for medical image processing and visualization.

  • Case Management: Load and save whole cases of segmented structures e.g. for surgery planning, educational training or intra operative visualization.
  • Basic Visualization in 2D and 3D: Visualize segmented structures in multiple manner e.g. iso surface rendering, stippling, hatching, silhouettes, volume rendering, 2d overlays.

The 'epibasix' R Package

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This package contains elementary tools for analysis of common epidemiological problems, ranging from sample size estimation, through 2x2 contingency table analysis and basic measures of agreement (kappa, sensitivity/specificity).

Appropriate print and summary statements are also written to facilitate interpretation wherever possible.

This package is a work in progress, so any comments or suggestions would be appreciated. Source code is commented throughout to facilitate modification. The target audience includes graduate students in various epi/biostatistics courses.