An SVM distal lung image classification using texture descriptors

Chesner Désir (1) , Caroline Petitjean (1) , Laurent Heutte (1) , Mathieu Salaün (1, 2) , Luc Thiberville (1, 2, 3)

1 LITIS - Laboratoire d'Informatique, de Traitement de l'Information et des Systèmes
2 QuantIF-LITIS - Equipe Quantification en Imagerie Fonctionnelle
3 Service de pneumologie, oncologie thoracique et soins intensifs respiratoires [Rouen]

A novel imaging technique can now provide microscopic images of the distal lung in vivo, for which quantitative analysis tools need to be developed. In this paper, we present an image classification system that is able to discriminate between normal and pathological images. Different feature spaces for discrimination are investigated and evaluated using a Support Vector Machine. Best classification rates reach up to 90% and 95% on non-smoker and smoker groups, respectively. A feature selection process is also implemented, that allows us to gain some insight about these images. Whereas further tests on extended databases are needed, these first results indicate that efficient computer based automated classification of normal vs. pathological images of the distal lung is feasible.

Mots clés: feature selection; LBP; SIFT; lung; alveoli; Image classification; feature extraction

Domaines: Traitement du signal et de l'image [eess.SP]; Traitement du signal et de l'image [eess.SP]; Apprentissage [cs.LG]

Chesner Désir, Caroline Petitjean, Laurent Heutte, Mathieu Salaün, Luc Thiberville. An SVM distal lung image classification using texture descriptors. Computerized Medical Imaging and Graphics, 2011, 10.1016/j.compmedimag.2011.11.001. ⟨10.1016/j.compmedimag.2011.11.001⟩. ⟨hal-00673369⟩