Classification of endomicroscopic images of the lung based on random subwindows and extra-trees.
Recently, the in vivo imaging of pulmonary alveoli was made possible thanks to confocal microscopy. For these images, we wish to aid the clinician by developing a computer-aided diagnosis system, able to discriminate between healthy and pathological subjects. The lack of expertise currently available on these images has first led us to choose a generic approach, based on pixel-value description of randomly extracted subwindows and decision tree ensemble for classification (extra-trees). In order to deal with the great complexity of our images, we adapt this method by introducing a texture-based description of the subwindows, based on local binary patterns. We show through our experimental protocol that this adaptation is a promising way to classify fibered confocal fluorescence microscopy images. In addition, we introduce a rejection mechanism on the classifier output to prevent nondetection errors.
LITIS EA 4108, Université de Rouen, 76801 Saint-Etienne-du-Rouvray, France. Chesner.Desir@univ-rouen.fr
Chesner
C
Désir
Caroline
C
Petitjean
Laurent
L
Heutte
Mathieu
M
Salaün
Luc
L
Thiberville
D000465:Algorithms; D016208:Databases, Factual; D003663:Decision Trees; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D006801:Humans; D018613:Microscopy, Confocal / Q000379:methods*; D011650:Pulmonary Alveoli / Q000033:anatomy & histology* / Q000166:cytology / Q000473:pathology* / Q000648:ultrastructure; D015203:Reproducibility of Results; D012907:Smoking / Q000473:pathology; D013906:Thoracoscopy / Q000379:methods*