Inspired by the co-occurrence matrix (CM) model for texture description, we introduce another important local metric, gradient direction, into polyp descriptor construction. Gradient direction and its two independent components, azimuth angle and polar angle, are used instead of the gray-level intensity to calculate the CMs of the Haralick model. Thus we obtain three new models: azimuth CM model (ACM), polar CM model (PCM) and gradient direction CM model (GDCM). These three new models share similar parameters with the traditional gray-level CM (GLCM) model which has 13 directions for volumetric data and 4 directions for image slices. To train and test the data, random forest method is employed. These three models are affected by angle quantization and, therefore, more than 10 experimental schemes are designed to get reasonable parameters for angle discretization. We compared our three models (ACM, PCM, GDCM) with the traditional GLCM model, a gradient magnitude CM (GMCM) model, and local anisotropic gradient orientations CM model (CoLIAge). Experimental results showed that our three models exceed the other three methods (GLCM, GMCM, CoLIAge) by their receiver operating characteristic (ROC) curves, AUC (area under the ROC curve) scores and accuracy values. Based on their AUC and accuracy, ACM should be the first choice for polyp classification.

Date Published: 30 May 2019
PDF
Proc. SPIE 10950, Medical Imaging 2019: Computer-Aided Diagnosis, 1095014 (30 May 2019); doi: 10.1117/12.2513056

Published in SPIE Proceedings Vol. 10950:
Medical Imaging 2019: Computer-Aided Diagnosis
Kensaku Mori; Horst K. Hahn, Editor(s)