You are here

Cross-platform

Ruby DICOM

Rating: 
No votes yet

UBY DICOM is a cross-platform library for handling DICOM files and network communication in the Ruby language. DICOM is a standard that is widely used throughout the world for saving and transmitting image data used in medicine. The library supports reading, editing and writing files as well as querying, retrieving and sending files.

RapidSMS

Rating: 
Your rating: None Average: 2.5 (2 votes)

RapidSMS is a SMS-based (text message) framework that manages data collection, complex workflows, and group coordination using basic mobile phones — and can present information on the internet as soon as it is received. So far RapidSMS has been customized and deployed with diverse functionality: remote health diagnostics, nutrition surveillance, supply chain tracking, registering children in public health campaigns, and community discussion.

MediPy

Rating: 
Your rating: None Average: 3.3 (3 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.

FreeSHIM

Rating: 
Your rating: None Average: 1 (2 votes)

FreeSHIM is an opensource electronic medical device interface, which aims to allow any EMR/PM system to talk to any medical device attached to a workstation without having to install tons of pesky drivers or “reinvent the wheel” for each additional device manufacturer.

It is written in Java, and has been tested on Linux and Windows workstations (though we’re pretty sure it also runs fine on Mac OS X as well), and exposes both SOAP and REST interfaces. Its only prerequisite is a running J2EE container, such as Apache Tomcat.

ZEPRS

Rating: 
Your rating: None Average: 4.2 (6 votes)

The Zambia Electronic Perinatal Record System (ZEPRS) is an Electronic Medical Record (EMR) system used by public obstetric clinics and a hospital (the University Teaching Hospital) in Lusaka, Zambia to improve patient care. The University of Alabama Birmingham (UAB) Center for Research in Women's Health (CRWH), RTI, and the Center for Infectious Disease Research in Zambia (CIDRZ) developed ZEPRS with local medical expertise and project engagement from the Lusaka Urban Health District, Central Board of Health and funding support from the Bill & Melinda Gates Foundation.

WEASIS

Rating: 
Your rating: None Average: 4.4 (30 votes)

Weasis is a multipurpose web-based viewer with a highly modular architecture. It has been designed to meet several expectations of clinical information systems and their future evolution regarding medical imaging: providing a web-based access to radiological images, as well as offering multimedia capabilities.

Weasis can be easily interfaced to any PACS supporting WADO via a web portal or as an XDS-I consumer in an IHE (Integrating the Healthcare Enterprise) environment (not yet fully implemented). It has been successfully interfaced to dcm4chee and commercial PACS.

Open Health Tools Project Implementation of IHE Profiles

Rating: 
Your rating: None Average: 1.6 (18 votes)

To lower the barrier to integration and expedite health care interoperability, Open Health Tools provides client side implementations of several key IHE profiles. These implementations were used successfully by over 35 systems to date at the 2007-2010 Connectathons in North America and Europe.

Braid

Rating: 
Your rating: None Average: 1.3 (8 votes)

Braid is a Client library for a portion of the IHE (Integrating the Healthcare Enterprise) transactions.

This library specifically focuses on PIX, XDS.a & XDS.b, ATNA, BPPC and XDS-SD profiles in ITI domain, and XDS-MS (Referral & Discharge Summary) and EDR content profiles in PCC domain.

WEKA

Rating: 
Your rating: None Average: 3 (2 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.

Pages

Subscribe to Cross-platform