Lung Tissue Classification in HRCT data Integrating the Clinical Context

In this paper, we investigate the influence of the clinical context of high–resolution computed tomography (HRCT) images of the chest on tissue classification. Evaluation of the classification performance is based on a high–quality multimedia data extracted from clinical routine. The clin-ical attributes with highest information gain ratio show to be relevant and consistent for the classification of lung tis-sue patterns. A combination of visual and clinical attributes allowed a mean of 93 % of correct predictions of testing in-stances among the five classes of lung tissue with optimized support vector machines (SVM), which represents a signifi-cant benefit of 8 % compared to a pure visually–based clas-sification.