Interstitial lung disease detection using template matching combined sparse coding and blended multi class support vector machine

Abstract

Interstitial lung disease (ILD), representing a collection of disorders, is considered to be the deadliest one, which increases the mortality rate of humans. In this paper, an automated scheme for detection and classification of ILD patterns is presented, which eliminates low inter-class feature variation and high intra-class feature variation in patterns, caused by translation and illumination effects. A novel and efficient feature extraction method named Template-Matching Combined Sparse Coding (TMCSC) is proposed, which extracts features invariant to translation and illumination effects, from defined regions of interest (ROI) within lung parenchyma. The translated image patch is compared with all possible templates of the image using template matching process. The corresponding sparse matrix for the set of translated image patches and their nearest template is obtained by minimizing the objective function of the similarity matrix of translated image patch and the template. A novel Blended-Multi Class Support Vector Machine (B-MCSVM) is designed for tackling high-intra class feature variation problems, which provides improved classification accuracy. Region of interests (ROIs) of five lung tissue patterns (healthy, emphysema, ground glass, micronodule, and fibrosis) selected from an internal multimedia database that contains high-resolution computed tomography (HRCT) image series are identified and utilized in this work. Performance of the proposed scheme outperforms most of the state-of-art multi-class classification algorithms.