Characterization of endomicroscopic images of the distal lung for computer-aided diagnosis

Aurélien Saint-Réquier (1) , Benoît Lelandais (1) , Caroline Petitjean (1) , Chesner Désir (1) , Laurent Heutte (1) , Mathieu Salaün (1) , Luc Thiberville (1)

1 LITIS - Laboratoire d'Informatique, de Traitement de l'Information et des Systèmes

This paper presents a new approach for the classification of pathological vs. healthy endomicroscopic images of the alveoli. These images, never seen before, require an adequate description. We investigate two types of feature vector for discrimination: a high-level feature vector based on visual analysis of the images, and a pixel-based, generic feature vector, based on Local Binary Patterns (LBP). Both feature sets are evaluated on state-of-the-art classifiers and an intensive study of the LBP parameters is conducted. Indeed best results are obtained with the LBP-based approach, with correct classification rates reaching up to 91.73\% and 97.85\% for non-smoking and smoking groups, respectively. Even though tests on extended databases are needed, first results are very encouraging for this difficult task of classifying endomicroscopic images of the distal lung.

Domaines: Traitement du signal et de l'image [eess.SP]; Imagerie médicale; Vision par ordinateur et reconnaissance de formes [cs.CV]; Traitement du signal et de l'image [eess.SP]

Aurélien Saint-Réquier, Benoît Lelandais, Caroline Petitjean, Chesner Désir, Laurent Heutte, et al.. Characterization of endomicroscopic images of the distal lung for computer-aided diagnosis. International Conference on Intelligent Computing, Sep 2009, Ulsan, South Korea. pp.994-1003. ⟨hal-00439110⟩