Semantic models for ranking medical images using Dirichlet non-parametric mixture models

Adrian S. Barb, Chi Ren Shyu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

With recent advances in diagnostic medical imaging, huge quantities of medical images are produced and stored in digital image repositories. While these repositories are difficult to be analyzed manually by medical experts, they can be evaluated using computer-based methods to enrich the process of decision making. For example, query by image methods can be used by medical experts for differential diagnosis by displaying previously evaluated cases that contain similar visual patterns. Also, less experienced practitioners can benefit from query-by-semantic methods in training processes especially for difficult-to-interpret cases with multiple pathologies. In this article we develop a methodology for ranking medical images based on Dirichlet process nonparametric distributions. Our approach uses natural groupings of images in a generated feature space to evaluate associative semantic mappings. Relevant semantic mappings are then used to generate additive computer models of semantic understanding of visual patterns found in images. We evaluate the performance of our method using mean average precision and precision-recall charts.

Original languageEnglish (US)
Title of host publication2011 IEEE 13th International Conference on e-Health Networking, Applications and Services, HEALTHCOM 2011
Pages344-350
Number of pages7
DOIs
StatePublished - Oct 17 2011
Event2011 IEEE 13th International Conference on e-Health Networking, Applications and Services, HEALTHCOM 2011 - Columbia, MO, United States
Duration: Jun 13 2011Jun 15 2011

Publication series

Name2011 IEEE 13th International Conference on e-Health Networking, Applications and Services, HEALTHCOM 2011

Other

Other2011 IEEE 13th International Conference on e-Health Networking, Applications and Services, HEALTHCOM 2011
CountryUnited States
CityColumbia, MO
Period6/13/116/15/11

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Health Informatics
  • Health Information Management

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