Investigation of 3D textural features' discriminating ability in diffuse lung disease quantification in MDCT

I. Mariolis, P. Korfiatis, L. Costaridou, C. Kalogeropoulou, D. Daoussis, T. Petsas

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

4 Scopus citations

Abstract

A current trend in lung CT image analysis is Computer Aided Diagnosis (CAD) schemes aiming at DLD patterns quantification. The majority of such schemes exploit textural features combined with supervised classification algorithms. In this direction, several 3D texture feature sets have been proposed. However their discriminating ability is not systematically evaluated, in terms of individual feature sets or in conjunction to different classifiers. In this paper, four classification settings combined with the RLE feature set, commonly used in the literature, and Laws feature set, first time employed for DLD characterization, are evaluated. Furthermore, the combination of RLE and Laws features was examined using the same classification settings. Although both RLE and Laws feature sets presented high discriminative ability for all classifiers considered (classification accuracy > 96.5%), their combination achieved even better results, yielding classification accuracy above 98.6%.

Original languageEnglish (US)
Title of host publication2010 IEEE International Conference on Imaging Systems and Techniques, IST 2010 - Proceedings
Pages135-138
Number of pages4
DOIs
StatePublished - Oct 18 2010
Event2010 IEEE International Conference on Imaging Systems and Techniques, IST 2010 - Thessaloniki, Greece
Duration: Jul 1 2010Jul 2 2010

Publication series

Name2010 IEEE International Conference on Imaging Systems and Techniques, IST 2010 - Proceedings

Conference

Conference2010 IEEE International Conference on Imaging Systems and Techniques, IST 2010
CountryGreece
CityThessaloniki
Period7/1/107/2/10

Keywords

  • 3D texture
  • Computed tomography
  • Diffuse lung disease
  • Laws features
  • Run length
  • Supervised classification

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition
  • Radiology Nuclear Medicine and imaging

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    Mariolis, I., Korfiatis, P., Costaridou, L., Kalogeropoulou, C., Daoussis, D., & Petsas, T. (2010). Investigation of 3D textural features' discriminating ability in diffuse lung disease quantification in MDCT. In 2010 IEEE International Conference on Imaging Systems and Techniques, IST 2010 - Proceedings (pp. 135-138). [5548528] (2010 IEEE International Conference on Imaging Systems and Techniques, IST 2010 - Proceedings). https://doi.org/10.1109/IST.2010.5548528