Diffuse lung disease classification in HRCT lung images using generalized Gaussian density modeling of wavelets coefficients

The generalized Gaussian density model for wavelet subbands has been applied widely in texture image retrieval. In this paper, we employ wavelet-based texture extraction that is based on accurate modeling of the distribution of wavelet coefficients using generalized Gaussian density to classify four diffuse lung disease patterns: normal, emphysema, ground glass opacity and honey-combing. The evaluated classifiers are K-nearest neighbor (K-NN) and support vector machine (SVM). A collection of 124 slices from 45 patients has been investigated, each slice of size 512×512, 12bit/pixel in DICOM format. The dataset contains 6000 ROIs of those slices marked by experienced radiologists. We employ this technique at different wavelet transform scales and compare results to other wavelet-based classification techniques for diffuse lung disease classification. The technique presented here has the best overall accuracy of 92.25% for the multi-class case with 3-level wavelet transform and SVM classifier.