Co-occurrence Matrix Invariance to Patient Size in Computed Tomography

The main objective of the proposed research is to evaluate the invariance of the cooccurrence texture model with respect to patient size in Computed Tomography (CT) data. Since patients’ scans can have high variation in pixel spacing, in order to standardize all patients’ texture descriptors, we investigate the effect of four interpolation techniques in reducing the pixel spacing variance: nearest neighbor, bilinear, cubic and B-spline methods. After applying interpolation, the co-occurrence texture model and nine Haralick texture descriptors are calculated to quantify the texture appearance of the soft tissues. The differences in the texture are evaluated before and after interpolation using Analysis of Variance (ANOVA) and the General Linear Model (GLM). Our preliminary results on liver images from five different patients show that the co-occurrence texture model is not affected by the difference in pixel spacing; therefore, computer-aided segmentation, retrieval and diagnosis tools that are based on co-occurrence texture models are expected to work on multiple patients of different sizes.