Detection of pathological condition in distal lung images
David Hebert, Chesner Désir, Caroline Petitjean, Laurent Heutte, Luc Thiberville · 2012
Recently, the in vivo imaging of pulmonary alveoli was made possible thanks to confocal microscopy. For these new images, we wish to aid the clinician by developing a computer-aided diagnosis system, able to detect a pathological state in these images. An original approach that combines a texture-based characterization of the images and uses a boosted cascade of classifiers to detect a pathological condition is presented in this paper. We propose and compare two state-of-the-art texture descriptors: cooccurence matrices and local binary patterns (LBP). Recognition rates with LBP reach up to 86.3% and 95.1% for the non-smoking and smoking groups, respectively. Even though tests on extended databases are needed, these preliminary results are encouraging for this challenging task of image classification.