Skip to content

A utility to create a searchable index of the DICOM standard into a searchable database with web app

License

Notifications You must be signed in to change notification settings

che85/DICOMSearch

 
 

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

33 Commits
 
 
 
 
 
 
 
 
 
 
 
 

Repository files navigation

DICOMSearch

A utility to put the DICOM standard into a searchable database with web app

This uses python to parse a local copy of the DocBook xml version of the standard (see DICOM Standard status page for the most up-to-date version: http://www.dclunie.com/dicom-status/status.html#BaseStandard2013).

The paragraphs are converted into couchdb documents and pushed to the server. Text search is enabled by Lucene index over paragraph text. As such, this implementation, as is, is tied to the specific instance of Cloudant database.

A utility couchSite copies the local site directory as attachments to a document called .site so that the site can be hosted directly from CouchDB. The site allows you to type a keyword and get instant results.

Dependencies

couchdb - https://pypi.python.org/pypi/CouchDB

lxml - https://pypi.python.org/pypi/lxml/3.4.4

Caveats

Some things that this doesn't support:

  • searches for words less than 5 letters long

  • boolean operations or wildcards

  • figures, tables, and other items from the standard

  • a DICOM data dictionary for quick lookup

  • search in the titles of the DocBook links (tables, sections, etc.)

ACKNOWLEDGMENTS

Development of this search index was supported in part by the Quantitative Image Informatics in Cancer Research (QIICR) project (http://qiicr.org) through the award U24 CA180918 from the National Cancer Institute.

About

A utility to create a searchable index of the DICOM standard into a searchable database with web app

Topics

Resources

License

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published

Languages

  • CSS 46.7%
  • JavaScript 40.3%
  • Python 7.8%
  • HTML 5.2%