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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:

WEKA

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Weka is a collection of machine learning algorithms for data mining tasks. The algorithms can either be applied directly to a dataset or called from your own Java code. Weka contains tools for data pre-processing, classification, regression, clustering, association rules, and visualization. It is also well-suited for developing new machine learning schemes.

Brainstorm

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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.

The 'DiagnosisMed' R Package

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DiagnosisMed is a package to analyze data from diagnostic test accuracy evaluating health conditions. It is being built to be used by health professionals. This package is able to estimate sensitivity and specificity from categorical and continuous test results including some evaluations of indeterminate results, or compare different categorical tests, and estimate reasonble cut-offs of tests and display it in a way commonly used by health professionals. No graphical interface is avalible yet. Partners are most welcome.

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.

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.

ADDIS

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ADDIS is a software developed within the Dutch Escher-project for managing and analyzing clinical trial information.

ADDIS is a proof-of-concept system that allows us to simultaneously discover the possibilities of and the requirements on a database of structured clinical trials data. The automated discovery and (meta-)analysis of trial data, as well as benefit-risk assessment is supported.

ADDIS comes with two built-in examples:

The 'epicalc' R Package

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Functions making R easy for epidemiological calculation.

Datasets from Dbase (.dbf), Stata (.dta), SPSS(.sav), EpiInfo(.rec) and Comma separated value (.csv) formats as well as R data frames can be processed to do make several epidemiological calculations.

The 'epiR' R Package

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A package for analysing epidemiological data. Contains functions for directly and indirectly adjusting measures of disease frequency, quantifying measures of association on the basis of single or multiple strata of count data presented in a contingency table, and computing confidence intervals around incidence risk and incidence rate estimates. Miscellaneous functions for use in meta-analysis, diagnostic test interpretation, and sample size calculations.

Gwyddion

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Gwyddion is a modular program for SPM (scanning probe microscopy) data visualization and analysis. Primarily it is intended for analysis of height fields obtained by scanning probe microscopy techniques (AFM, MFM, STM, SNOM/NSOM), however it can be generally used for any other height field and image analysis, for instance for analysis of profilometry data.

It aims to provide multiplatform modular program for 2D data analysis that could be easily extended by modules and plug-ins.

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