Classification of interstitial lung disease patterns using local DCT features and random forest.

Anthimopoulos, Marios; Christodoulidis, Stergios; Christe, Andreas; Mougiakakou, Stavroula (2014). Classification of interstitial lung disease patterns using local DCT features and random forest. IEEE Engineering in Medicine and Biology Society conference proceedings, 2014, pp. 6040-6043. IEEE Service Center 10.1109/EMBC.2014.6945006

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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.

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