Using a Priori Knowledge to Classify in Vivo Images of the Lung

Abstract

Until recently, the alveolar region could not be investigated in-vivo. A novel technique, based on confocal microscopy, can now provide new images of the respiratory alveolar system, for which quantitative analysis tools must be developed, for diagnosis and follow up of pathological situations. In particular, we wish to aid the clinician by developing a computer-aided diagnosis system, able to discriminate between healthy and pathological subjects. This paper describes this system, in which images are first characterized through a 148-feature vector then classified by an SVM (Support Vector Machine). Experiments conducted on smoker and non smoker images show that the dimensionality of the feature vector can be reduced significantly without decreasing classification accuracy, and thus gaining some insight about the usefulness of features for medical diagnosis. These promising results allow us to consider interesting perspectives for this very challenging medical application.