Acquisition information spectrum for evaluating sonographic quality

Nghia Q. Nguyen, Craig K. Abbey, Michael Insana

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

Abstract

We are developing a first-principles task-based approach to the optimal design and evaluation of ultrasonic imaging systems. Examining five clinical features related to breast lesion diagnosis, we quantified information flow at several stages in the image formation process. We found that the diagnostic performance of a given system configuration will vary with the patient feature, sometimes significantly. Our analysis expresses diagnostic performance of an imaging system for a specific clinical task as a function of patient properties that are separable from instrument properties. Hence it is possible to show how image quality metrics, like spatial and contrast resolution, combine with patient features to determine feature discriminability. In this paper, we describe an information theoretic approach to diagnostic performance evaluation that has given us a new quantity, the acquisition information spectrum (AIS). Like NEQ in radiography, AIS in sonography provides a foundation for medical ultrasonic imaging system design.

Original languageEnglish (US)
Title of host publication2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2012
Pages2302-2305
Number of pages4
DOIs
StatePublished - Dec 14 2012
Event34th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 2012 - San Diego, CA, United States
Duration: Aug 28 2012Sep 1 2012

Publication series

NameProceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
ISSN (Print)1557-170X

Other

Other34th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 2012
Country/TerritoryUnited States
CitySan Diego, CA
Period8/28/129/1/12

ASJC Scopus subject areas

  • Signal Processing
  • Biomedical Engineering
  • Computer Vision and Pattern Recognition
  • Health Informatics

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