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

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%.