title: MDCT-based 3-D texture classification of emphysema and early smoking related lung pathologies.
abstract: Our goal is to enhance the ability to differentiate normal lung from subtle pathologies via multidetector row CT (MDCT) by extending a two-dimensional (2-D) texturebased tissue classification [adaptive multiple feature method (AMFM)] to use three-dimensional (3-D) texture features. We performed MDCT on 34 humans and classified volumes of interest (VOIs) in the MDCT images into five categories: EC, emphysema in severe chronic obstructive pulmonary disease (COPD); MC, mild emphysema in mild COPD; NC, normal appearing lung in mild COPD; NN, normal appearing lung in normal nonsmokers; and NS, normal appearing lung in normal smokers. COPD severity was based upon pulmonary function tests (PFTs). Airways and vessels were excluded from VOIs; 24 3-D texture features were calculated; and a Bayesian classifier was used for discrimination. A leave-one-out method was employed for validation. Sensitivity of the four-class classification in the form of 3-D/2-D was: EC: 85%/71%, MC: 90%/82%; NC: 88%/50%; NN: 100%/60%. Sensitivity and specificity for NN using a two-class classification of NN and NS in the form of 3-D/2-D were: 99%/72% and 100%/75%, respectively. We conclude that 3-D AMFM analysis of lung parenchyma improves discrimination compared to 2-D AMFM of the same VOIs. Furthermore, our results suggest that the 3-D AMFM may provide a means of discriminating subtle differences between smokers and nonsmokers both with normal PFTs.
authors.affiliation: Iowa Comprehension Lung Imaging Center, University of Iowa, Iowa City, IA 52240, USA.
authors.forename: Ye
authors.initials: Y
authors.lastname: Xu
authors.forename: Milan
authors.initials: M
authors.lastname: Sonka
authors.forename: Geoffrey
authors.initials: G
authors.lastname: McLennan
authors.forename: Junfeng
authors.initials: J
authors.lastname: Guo
authors.forename: Eric A
authors.initials: EA
authors.lastname: Hoffman
mesh_terms: D000465:Algorithms; D016477:Artifacts; D001185:Artificial Intelligence; D005260:Female; D006801:Humans; D021621:Imaging, Three-Dimensional / Q000379:methods*; D016247:Information Storage and Retrieval / Q000379:methods; D008297:Male; D008875:Middle Aged; D010363:Pattern Recognition, Automated / Q000379:methods; D011656:Pulmonary Emphysema / Q000145:classification* / Q000000981:diagnostic imaging* / Q000209:etiology; D011829:Radiation Dosage; D011856:Radiographic Image Enhancement / Q000295:instrumentation / Q000379:methods*; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D012720:Severity of Illness Index; D012907:Smoking / Q000009:adverse effects*; D013269:Stochastic Processes; D014057:Tomography, X-Ray Computed / Q000295:instrumentation / Q000379:methods*; D014159:Transducers