title: Classification of interstitial lung disease patterns using local DCT features and random forest.
abstract: Over the last decade, a plethora of computer-aided diagnosis (CAD) systems have been proposed aiming to improve the accuracy of the physicians in the diagnosis of interstitial lung diseases (ILD). In this study, we propose a scheme for the classification of HRCT image patches with ILD abnormalities as a basic component towards the quantification of the various ILD patterns in the lung. The feature extraction method relies on local spectral analysis using a DCT-based filter bank. After convolving the image with the filter bank, q-quantiles are computed for describing the distribution of local frequencies that characterize image texture. Then, the gray-level histogram values of the original image are added forming the final feature vector. The classification of the already described patches is done by a random forest (RF) classifier. The experimental results prove the superior performance and efficiency of the proposed approach compared against the state-of-the-art.
authors.forename: M
authors.initials: M
authors.lastname: Anthimopoulos
authors.forename: S
authors.initials: S
authors.lastname: Christodoulidis
authors.forename: A
authors.initials: A
authors.lastname: Christe
authors.forename: S
authors.initials: S
authors.lastname: Mougiakakou
mesh_terms: D016022:Case-Control Studies; D003663:Decision Trees; D006801:Humans; D008168:Lung / Q000000981:diagnostic imaging; D017563:Lung Diseases, Interstitial / Q000000981:diagnostic imaging*; D010363:Pattern Recognition, Automated; D011857:Radiographic Image Interpretation, Computer-Assisted; D014057:Tomography, X-Ray Computed / Q000379:methods