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OpenEMed

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Your rating: None Average: 3 (2 votes)

OpenEMed is a set of distributed healthcare information service components built around the OMG distributed object specifications and the HL7 (and other) data standards and is written in Java for platform portability. We emphasize the interoperable service functionality that this approach provides in reducing the time it takes to build a healthcare related system. It is not intended as a turnkey system but rather a set of components that can be assembled and configured to meet a variety of tasks.

Homepage: 
http://openmed.sourceforge.net/

OpenXDS

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Your rating: None Average: 3.4 (19 votes)

The IHE Cross Enterprise Document Sharing (XDS.b) integration profile facilitates the registration, distribution and access across health enterprises of patient electronic health records. XDS.b is focused on providing a standards-based specification for managing the sharing of medical summary documents between any healthcare enterprise, ranging from a private physician office to a clinic to an acute care in-patient facility.

Homepage: 
https://www.projects.openhealthtools.org/sf/projects/openxds/

Snofyre

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Snofyre is an open source, service oriented API for creating SNOMED CT enabled applications in Java. It provides a number of SNOMED CT related services out of the box. These services can be used:

  • as a starter for understanding how to add SNOMED CT functionality to an application.
  • to rapidly prototype a SNOMED CT enabled application.

Snofyre API aims to

  • reduce the 'ramp up' time needed to understand
  • and embed SNOMED CT functionality in an application.
Homepage: 
http://code.google.com/p/snofyre/

PyEEG

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Your rating: None Average: 2 (4 votes)

A Python function library to extract EEG feature from EEG time series in standard Python and numpy data structure. Features include classical spectral analysis, entropies, fractal dimensions, DFA, inter-channel synchrony and order, etc.

Homepage: 
http://code.google.com/p/pyeeg/

WEKA

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Your rating: None Average: 2.7 (3 votes)

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.

Homepage: 
http://www.cs.waikato.ac.nz/ml/weka/

Charrua DICOM Toolkit

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DICOM basic constructs used to create the tools at CharruaSoft.com. Its C++ code is a re-interpretation of the original UCDMC library by Mark Oskin. It tries to be much simpler and compact, also uses many Borland VCL specific structures.

Homepage: 
http://www.charruasoft.com/en/products/cdtk.htm

Dicom4j

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Your rating: None Average: 1.3 (4 votes)

The purpose of the dicom4j platform is to provide java components related to the Dicom Standard. For those purpose, the platform is based on 4 areas:

  • framework: framework which implements the standards
  • toolkit: offer ways to easily develop software based on the framework
  • plugins: end-user components which adress commons needs you can find in most dicom applications
  • apps: stand alone applications for end-user or tests purpose
Homepage: 
http://dicom4j.sourceforge.net/

MediPy

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Your rating: None Average: 3.2 (5 votes)

MediPy is a cross-platform software (Windows, Linux, Mac OS), dedicated to the visualization and processing aspects of medical imaging. It is targeted at both physicians and researchers, being both user-friendly and easy to extend. Physicians will benefit from the pre-programmed tasks (e.g. segmentation, registration, detection of lesions) and the possibility to record new tasks, tailoring the software to each user. The use of standard file formats (Analyze/Nifti, Dicom) allows to load image from many sources, as well as integrate to a PACS.

Homepage: 
https://code.google.com/p/medipy/

ImageJ

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Your rating: None Average: 3.4 (14 votes)

ImageJ is a public domain Java image processing program inspired by NIH Image for the Macintosh. It runs, either as an online applet or as a downloadable application, on any computer with a Java 1.4 or later virtual machine. Downloadable distributions are available for Windows, Mac OS, Mac OS X and Linux.

Homepage: 
http://rsb.info.nih.gov/ij/

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