Multiscale sparse representation of high-resolution computed tomography (HRCT) lung images for diffuse lung disease classification

A multiscale sparse representation scheme based on wavelet and contourlet transforms is employed to describe four patterns of diffuse lung disease patterns: normal, emphysema, ground glass opacity (GGO) and honey-combing based on HRCT lung images. First, using sparse representation, four discriminative dictionaries are trained for the four patterns respectively. After that, in the classification phase, a patch or ROI is assigned to the pattern with minimum resconstruction error. The method is tested on a collection of 89 slices from 38 patients, each slice of size 512 × 512, 16 bits/pixel in DICOM format. The dataset contains 73,000 ROIs of those slices marked by experienced radiologists. We employ this technique with 2-scale wavelet and [2 3] contourlet transform for diffuse lung disease classification. The technique presented here has the overall sensitivity of 91.05% and specificity 97.01%.