title: Performance of topological texture features to classify fibrotic interstitial lung disease patterns.
abstract: OBJECTIVE
  Topological texture features were compared in their ability to classify "honeycombing," a morphological pattern that is considered indicative for the presence of fibrotic interstitial lung disease in high-resolution computed tomography (HRCT) images.
  
  
  METHODS
  For 14 patients with known occurrence of honeycombing, a stack of 70 axial, lung kernel reconstructed images was acquired from HRCT chest exams. A set of 964 regions of interest of both healthy and pathological (356) lung tissue was identified by an experienced radiologist. Texture features were extracted using statistical features (Stat), six properties calculated from gray-level co-occurrence matrices (GLCMs), Minkowski dimensions (MDs), and three Minkowski functionals (MFs) (e.g., MF.Euler). A naïve Bayes (NB) and k-nearest-neighbor (k-NN) classifier, a multilayer radial basis functions network (RBFN), and a support vector machine with a radial basis function (SVMrbf) kernel were optimized in a tenfold 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.
  
  
  RESULTS
  The best classification results were obtained by the MF features, which performed significantly better than all the standard Stat, GLCM, and MD features (p < 0.001) for both classifiers. The highest accuracies were found for MF.Euler (93.6%, 94.9%, 94.2%, and 95.0% for NB, k-NN, RBFN, and SVMrbf, respectively). The best groups of standard texture features were a Stat and GLCM ("homogeneity") feature set (up to 91.8%).
  
  
  CONCLUSIONS
  The results indicate that advanced topological texture features derived from MFs can provide superior classification performance in computer-assisted diagnosis of fibrotic interstitial lung disease patterns when compared to standard texture analysis methods.
authors.affiliation: Department of Imaging Sciences, University of Rochester, Rochester, New York 14627, USA. mbh@bme.rochester.edu
authors.forename: Markus B
authors.initials: MB
authors.lastname: Huber
authors.forename: Mahesh B
authors.initials: MB
authors.lastname: Nagarajan
authors.forename: Gerda
authors.initials: G
authors.lastname: Leinsinger
authors.forename: Roger
authors.initials: R
authors.lastname: Eibel
authors.forename: Lawrence A
authors.initials: LA
authors.lastname: Ray
authors.forename: Axel
authors.initials: A
authors.lastname: Wismüller
mesh_terms: D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008168:Lung / Q000166:cytology / Q000000981:diagnostic imaging / Q000473:pathology; D017563:Lung Diseases, Interstitial / Q000150:complications* / Q000000981:diagnostic imaging* / Q000473:pathology; D011658:Pulmonary Fibrosis / Q000150:complications* / Q000000981:diagnostic imaging* / Q000473:pathology; D014057:Tomography, X-Ray Computed