title: A texton-based approach for the classification of lung parenchyma in CT images.
abstract: In this paper, a texton-based classification system based on raw pixel representation along with a support vector machine with radial basis function kernel is proposed for the classification of emphysema in computed tomography images of the lung. The proposed approach is tested on 168 annotated regions of interest consisting of normal tissue, centrilobular emphysema, and paraseptal emphysema. The results show the superiority of the proposed approach to common techniques in the literature including moments of the histogram of filter responses based on Gaussian derivatives. The performance of the proposed system, with an accuracy of 96.43%, also slightly improves over a recently proposed approach based on local binary patterns.
authors.affiliation: Department of Electrical and Computer Engineering, University of Waterloo, Canada. mgangeh@pami.uwaterloo.ca
authors.forename: Mehrdad J
authors.initials: MJ
authors.lastname: Gangeh
authors.forename: Lauge
authors.initials: L
authors.lastname: Sørensen
authors.forename: Saher B
authors.initials: SB
authors.lastname: Shaker
authors.forename: Mohamed S
authors.initials: MS
authors.lastname: Kamel
authors.forename: Marleen
authors.initials: M
authors.lastname: de Bruijne
authors.forename: Marco
authors.initials: M
authors.lastname: Loog
mesh_terms: D000465:Algorithms; D004646:Emphysema / Q000000981:diagnostic imaging*; D006801:Humans; D021621:Imaging, Three-Dimensional / Q000379:methods*; D008168:Lung / Q000000981:diagnostic imaging*; D010363:Pattern Recognition, Automated / Q000379:methods*; D011856:Radiographic Image Enhancement / Q000379:methods; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D013902:Radiography, Thoracic / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D014057:Tomography, X-Ray Computed / Q000379:methods*