Texture Analysis in Lung HRCT Images

Automatic classification of lung tissue patterns in high resolution computed tomography images of patients with interstitial lung diseases is an important stage in the construction of a computer-aided diagnosis system. To this end, a novel approach is proposed using two sets of overcomplete wavelet filters, namely discrete wavelet frames (DWF) and rotated wavelet frames (RWF), to extract the features which best characterizes the lung tissue patterns. Support vector machines learning algorithm is then applied to perform the pattern classification. Four different lung patterns (ground glass, honey combing, reticular, and normal) selected from a database of 340 images are classified using the proposed method. The overall multiclass accuracy reaches 90.72%, 95.85%, and 96.81% for DWF, RWF, and their combination, respectively. These results prove that RWF is superior to DWF, due to its orientation selectivity. However, best results are obtained by the combination of two filter banks which shows that the two set of filters are complementary.