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 high-quality visual data extracted from clinical routine. The clinical attributes with highest information gain ratio show to be relevant and consistent for the classification of lung tissue patterns. A combination of visual and clinical attributes allowed a mean of 93% correct predictions of testing instances among the five classes of lung tissue with optimized support vector machines (SVM), which represents a significant benefit of 8% compared to a pure visually-based classification.