TY - GEN
T1 - Investigation of 3D textural features' discriminating ability in diffuse lung disease quantification in MDCT
AU - Mariolis, I.
AU - Korfiatis, P.
AU - Costaridou, L.
AU - Kalogeropoulou, C.
AU - Daoussis, D.
AU - Petsas, T.
PY - 2010
Y1 - 2010
N2 - 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%.
AB - 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%.
KW - 3D texture
KW - Computed tomography
KW - Diffuse lung disease
KW - Laws features
KW - Run length
KW - Supervised classification
UR - http://www.scopus.com/inward/record.url?scp=77957849599&partnerID=8YFLogxK
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U2 - 10.1109/IST.2010.5548528
DO - 10.1109/IST.2010.5548528
M3 - Conference contribution
AN - SCOPUS:77957849599
SN - 9781424464944
T3 - 2010 IEEE International Conference on Imaging Systems and Techniques, IST 2010 - Proceedings
SP - 135
EP - 138
BT - 2010 IEEE International Conference on Imaging Systems and Techniques, IST 2010 - Proceedings
T2 - 2010 IEEE International Conference on Imaging Systems and Techniques, IST 2010
Y2 - 1 July 2010 through 2 July 2010
ER -