Local scaling properties of texture regions were compared in their ability to classify morphological patterns
known as 'honeycombing' that are considered indicative for the presence of fibrotic interstitial lung diseases
in high-resolution computed tomography (HRCT) images. For 14 patients with known occurrence of honeycombing,
a stack of 70 axial, lung kernel reconstructed images were acquired from HRCT chest exams. 241
regions of interest of both healthy and pathological (89) lung tissue were identified by an experienced radiologist.
Texture features were extracted using six properties calculated from gray-level co-occurrence matrices (GLCM),
Minkowski Dimensions (MDs), and the estimation of local scaling properties with Scaling Index Method (SIM).
A k-nearest-neighbor (k-NN) classifier and a Multilayer Radial Basis Functions Network (RBFN) were optimized
in a 10-fold cross-validation for each texture vector, and the classification accuracy was calculated on independent
test sets as a quantitative measure of automated tissue characterization. A Wilcoxon signed-rank test was used
to compare two accuracy distributions including the Bonferroni correction. The best classification results were
obtained by the set of SIM features, which performed significantly better than all the standard GLCM and
MD features (p < 0.005) for both classifiers with the highest accuracy (94.1%, 93.7%; for the k-NN and RBFN
classifier, respectively). The best standard texture features were the GLCM features 'homogeneity' (91.8%,
87.2%) and 'absolute value' (90.2%, 88.5%). The results indicate that advanced texture features using local
scaling properties can provide superior classification performance in computer-assisted diagnosis of interstitial
lung diseases when compared to standard texture analysis methods.