title: Computer-aided detection of interstitial abnormalities in chest radiographs using a reference standard based on computed tomography.
abstract: A computer-aided detection (CAD) system is presented for the localization of interstitial lesions in chest radiographs. The system analyzes the complete lung fields using a two-class supervised pattern classification approach to distinguish between normal texture and texture affected by interstitial lung disease. Analysis is done pixel-wise and produces a probability map for an image where each pixel in the lung fields is assigned a probability of being abnormal. Interstitial lesions are often subtle and ill defined on x-rays and hence difficult to detect, even for expert radiologists. Therefore a new, semiautomatic method is proposed for setting a reference standard for training and evaluating the CAD system. The proposed method employs the fact that interstitial lesions are more distinct on a computed tomography (CT) scan than on a radiograph. Lesion outlines, manually drawn on coronal slices of a CT scan of the same patient, are automatically transformed to corresponding outlines on the chest x-ray, using manually indicated correspondences for a small set of anatomical landmarks. For the texture analysis, local structures are described by means of the multiscale Gaussian filter bank. The system performance is evaluated with ROC analysis on a database of digital chest radiographs containing 44 abnormal and 8 normal cases. The best performance is achieved for the linear discriminant and support vector machine classifiers, with an area under the ROC curve (A(z)) of 0.78. Separate ROC curves are built for classification of abnormalities of different degrees of subtlety versus normal class. Here the best performance in terms of A(z) is 0.90 for differentiation between obviously abnormal and normal pixels. The system is compared with two human observers, an expert chest radiologist and a chest radiologist in training, on evaluation of regions. Each lung field is divided in four regions, and the reference standard and the probability maps are converted into region scores. The system performance does not significantly differ from that of the observers, when the perihilar regions are excluded from evaluation, and reaches A(z) = 0.85 for the system, with A(z) = 0.88 for both observers.
authors.affiliation: Images Sciences Institute, University Medical Center Utrecht, Utrecht, The Netherlands. yulia@isi.uu.nl
authors.forename: Yulia
authors.initials: Y
authors.lastname: Arzhaeva
authors.forename: Mathias
authors.initials: M
authors.lastname: Prokop
authors.forename: David M J
authors.initials: DM
authors.lastname: Tax
authors.forename: Pim A
authors.initials: PA
authors.lastname: De Jong
authors.forename: Cornelia M
authors.initials: CM
authors.lastname: Schaefer-Prokop
authors.forename: Bram
authors.initials: B
authors.lastname: van Ginneken
mesh_terms: D003936:Diagnosis, Computer-Assisted / Q000379:methods* / Q000592:standards*; D006801:Humans; D017563:Lung Diseases, Interstitial / Q000175:diagnosis* / Q000000981:diagnostic imaging* / Q000453:epidemiology; D015588:Observer Variation; D011336:Probability; D012372:ROC Curve; D013902:Radiography, Thoracic; D012015:Reference Standards; D014057:Tomography, X-Ray Computed