Multiscale AM-FM analysis of pneumoconiosis x-ray images

Victor Murray, Marios S. Pattichis, Herbert Davis, Eduardo S. Barriga, Peter Soliz

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

16 Scopus citations

Abstract

This paper presents a computer-aided diagnostic (CAD) system for analyzing chest radiographs based on the International Labor Organization (ILO) standards. We introduce an amplitude-modulation frequency-modulation (AM-FM) based methodology by which a computer-based system will extract AM-FM features and detect those with suspected interstitial lung diseases. For classification, we use Partial Least Squares (PLS) using a low number of extracted factors (making the system robust). We consider several different AM-FM classifiers based on extracting features from individual scales as well as a final classifier that combines results from the individuals scales. We validate our methodology on 11 standard images graded according to the ILO standard. For several scales, as well as for the combined classifier that uses information from all scales, we get excellent classification results (area under the receiver operator characteristics curve equal to 1.0) using a limited number of latent PLS factors.

Original languageEnglish
Title of host publication2009 IEEE International Conference on Image Processing, ICIP 2009 - Proceedings
PublisherIEEE Computer Society
Pages4201-4204
Number of pages4
ISBN (Print)9781424456543
DOIs
StatePublished - 2009
Externally publishedYes
Event2009 IEEE International Conference on Image Processing, ICIP 2009 - Cairo, Egypt
Duration: 7 Nov 200910 Nov 2009

Publication series

NameProceedings - International Conference on Image Processing, ICIP
ISSN (Print)1522-4880

Conference

Conference2009 IEEE International Conference on Image Processing, ICIP 2009
Country/TerritoryEgypt
CityCairo
Period7/11/0910/11/09

Keywords

  • AM-FM
  • X-ray chest imaging

Fingerprint

Dive into the research topics of 'Multiscale AM-FM analysis of pneumoconiosis x-ray images'. Together they form a unique fingerprint.

Cite this