title: Computer-aided classification of interstitial lung diseases via MDCT: 3D adaptive multiple feature method (3D AMFM).
abstract: OBJECTIVE
  Computer-aided detection algorithms applied to multidetector row CT (MDCT) lung image data sets have the potential to significantly alter clinical practice through the early, quantitative detection of pulmonary pathology. In this project, we have further developed a computer-aided detection tool, the adaptive multiple feature method (AMFM), for the detection of interstitial lung diseases based on MDCT-generated volumetric data.
  
  
  METHODS
  We performed MDCT (Siemens Sensation 16 or 64 120 kV, B50f convolution kernel, and <or=0.75-mm slice thickness) on 20 human volunteers recruited from four cohorts studied under an National Institutes of Health-sponsored Bioengineering Research Partnership Grant: 1) normal never smokers; 2) normal smokers; 3) those with emphysema, and 4) those with interstitial lung disease (total: 11 males, 9 females; age range 20-75 years, mean age 40 years). A total of 1,184 volumes of interest (VOIs; 21 x 21 pixels in plane) were marked by a senior radiologist and a senior pulmonologist as emphysema (EMPH, n = 287); ground-glass (GG, n = 147), honeycombing (HC, n = 137), normal nonsmokers (NN, n = 287), and normal smokers (NS, n = 326). For each VOI, we calculated 24 volumetric features, including statistical features (first-order features, run-length, and co-occurrence features), histogram, and fractal features. We compared two methods of classification (a Support Vector Machine (SVM) and a Bayesian classifier) using a 10-fold cross validation method and McNemar's test.
  
  
  RESULTS
  The sensitivity of five patterns in the form of Bayesian/SVM was: EMPH: 91/93%; GG: 89/86%; HC: 93/90%; NN: 90/73%; and NS: 75/82%. The specificity of five patterns in the form of Bayesian/support vector machine was: EMPH: 98/98%; GG: 98/98%; HC: 99/99%; NN: 90/94%; and NS: 96/91%.
  
  
  CONCLUSIONS
  We conclude that volumetric features including statistical features, histogram and fractal features can be successfully used in differentiation of parenchymal pathology associated with both emphysema and interstitial lung diseases. Additionally, support vector machine and Bayesian methods are comparable classifiers for characterization of interstitial lung diseases on MDCT images.
authors.affiliation: Department of Radiology, University of Iowa, CC701, General Hospital, Iowa City, IA 52242, USA.
authors.forename: Ye
authors.initials: Y
authors.lastname: Xu
authors.forename: Edwin J R
authors.initials: EJ
authors.lastname: van Beek
authors.forename: Yu
authors.initials: Y
authors.lastname: Hwanjo
authors.forename: Junfeng
authors.initials: J
authors.lastname: Guo
authors.forename: Geoffrey
authors.initials: G
authors.lastname: McLennan
authors.forename: Eric A
authors.initials: EA
authors.lastname: Hoffman
mesh_terms: D000328:Adult; D000368:Aged; D000465:Algorithms; D016477:Artifacts; D001499:Bayes Theorem; D005260:Female; D006801:Humans; D021621:Imaging, Three-Dimensional; D017563:Lung Diseases, Interstitial / Q000145:classification* / Q000000981:diagnostic imaging*; D008297:Male; D008875:Middle Aged; D010363:Pattern Recognition, Automated / Q000379:methods; D011857:Radiographic Image Interpretation, Computer-Assisted; D012680:Sensitivity and Specificity; D014057:Tomography, X-Ray Computed / Q000379:methods*