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Medical Imaging Interaction Toolkit (MITK)

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The Medical Imaging Interaction Toolkit (MITK) is a free open-source software system for development of interactive medical image processing software. MITK combines the Insight Toolkit (ITK) and the Visualization Toolkit (VTK) with an application framework. As a toolkit, MITK offers those features that are relevant for the development of interactive medical imaging software covered neither by ITK nor VTK.

Core features of the MITK platform:

    Visualization Toolkit (VTK)

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    Your rating: None Average: 2.3 (6 votes)

    The Visualization Toolkit (VTK) is an open-source, freely available software system for 3D computer graphics, image processing, and visualization. It consists of a C++ class library and several interpreted interface layers including Tcl/Tk, Java, and Python. VTK supports a wide variety of visualization algorithms including scalar, vector, tensor, texture, and volumetric methods, as well as advanced modeling techniques such as implicit modeling, polygon reduction, mesh smoothing, cutting, contouring, and Delaunay triangulation.

    PyEPL

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    Your rating: None Average: 2.8 (6 votes)

    PyEPL (the Python Experiment-Programming Library) is a library for coding psychology experiments in Python. It supports presentation of both visual and auditory stimuli, and supports both manual (keyboard/joystick) and sound (microphone) input as responses.

    The R Project for Statistical Computing

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    R is a language and environment for statistical computing and graphics. It is a GNU project which is similar to the S language and environment which was developed at Bell Laboratories (formerly AT&T, now Lucent Technologies) by John Chambers and colleagues. R can be considered as a different implementation of S. There are some important differences, but much code written for S runs unaltered under R."

    The 'epitools' R Package

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    Your rating: None Average: 4.7 (3 votes)

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

    The 'surveillance' R Package

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    The R-package ’surveillance’ is a framework for the development and the evaluation of outbreak detection algorithms in univariate and multivariate routine collected public health surveillance data. Hence, potential users are biostatisticians, epidemiologists and others working in applied infectious disease epidemiology. However, applications could just as well originate from environmetrics, reliability engineering, econometrics or social sciences.

    QuickViewHL7

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    Your rating: None Average: 4.3 (4 votes)

    HL7 file viewer, in tree-view format, with associated segment/field documentation. The latest release now includes editing, at all levels in the tree-view, e.g segment, field or component values. Purpose is for testing and bug-tracing HL7 communications.

    Laika

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    Your rating: None Average: 3.5 (4 votes)

    Laika analyzes and reports on the interoperability capabilities of EHR systems. This includes the testing for certification of EHR software products and networks.

    To support EHR data interoperability testing, Laika is designed to verify the input and output of EHR data against the standards and criteria identified by the Certification Commission for Health Information Technology (CCHIT). Laika is used by the Certification Commission to perform part of the interoperability certification inspection of EHRs.

    GNU Octave

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    GNU Octave is a high-level language, primarily intended for numerical computations. It provides a convenient command line interface for solving linear and nonlinear problems numerically, and for performing other numerical experiments using a language that is mostly compatible with Matlab. It may also be used as a batch-oriented language.

    PyMVPA

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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."

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