Topological texture features 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 honey-combing, a stack of 70
axial, lung kernel reconstructed images were acquired from HRCT chest exams. A set of 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 three Minkowski Functionals (MFs, e.g. MF.euler). 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
and the significance thresholds were adjusted for multiple comparisons by the Bonferroni correction.
The best classification results were obtained by the MF features, which performed significantly better than all
the standard GLCM and MD features (p < 0.005) for both classifiers. The highest accuracy was found for
MF.euler (97.5%, 96.6%; 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 topological texture features can provide superior classification performance in computer-assisted
diagnosis of interstitial lung diseases when compared to standard texture analysis methods.