Lung Tissue Classification in HRCT Data Integrating the Clinical Context

Authors:
Adrien Depeursinge;Jimison Iavindrasana;Gilles Cohen;Alexandra Platon;Pierre-Alexandre Poletti;Henning Müller
Affiliations:
-;-;-;-;-;-
Venue:
CBMS '08 Proceedings of the 2008 21st IEEE International Symposium on Computer-Based Medical Systems
Year:
2008

Citing 0
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Abstract

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.