Using Multiscale Visual Words for Lung Texture Classification and Retrieval

Interstitial lung diseases (ILDs) are regrouping over 150 heterogeneous disorders of the lung parenchyma. High-Resolution Computed Tomography (HRCT) plays an important role in diagnosis, as standard chest x-rays are often non-specific for ILDs. Assessment of ILDs is considerd hard for clinicians because the diseases are rare, patterns often look visually similar and various clinical data need to be integrated. An image retrieval system to support interpretation of HRCT images by retrieving similar images is presented in this paper. The system uses a wavelet transform based on Difference of Gaussians (DoG) in order to extract texture descriptors from a set of 90 image series containing 1679 manually annotated regions corresponding to various ILDs. Visual words are used for feature aggregation and to describe tissue patterns. The optimal scale-progression scheme, number of visual words, as well as distance measure for clustering to generate visual words are investigated. A suffciently high number of visual words is required to accurately describe patterns with high intra-class variations such as healthy tissue. Scale progression has less influence and the Euclidean distance performs better than other distances. The results show that the system is able to learn the wide intra-class variations of healthy tissue and the characteristics of abnormal lung tissue to provide reliable assistance to clinicians.