title: Computer-aided detection of ground glass nodules in thoracic CT images using shape, intensity and context features.
abstract: Ground glass nodules (GGNs) occur less frequent in computed tomography (CT) scans than solid nodules but have a much higher chance of being malignant. Accurate detection of these nodules is therefore highly important. A complete system for computer-aided detection of GGNs is presented consisting of initial segmentation steps, candidate detection, feature extraction and a two-stage classification process. A rich set of intensity, shape and context features is constructed to describe the appearance of GGN candidates. We apply a two-stage classification approach using a linear discriminant classifier and a GentleBoost classifier to efficiently classify candidate regions. The system is trained and independently tested on 140 scans that contained one or more GGNs from around 10,000 scans obtained in a lung cancer screening trial. The system shows a high sensitivity of 73% at only one false positive per scan.
authors: Fraunhofer MEVIS, Bremen, Germany.
authors: Colin
authors: C
authors: Jacobs
authors: Clara I
authors: CI
authors: Sánchez
authors: Stefan C
authors: SC
authors: Saur
authors: Thorsten
authors: T
authors: Twellmann
authors: Pim A
authors: PA
authors: de Jong
authors: Bram
authors: B
authors: van Ginneken
mesh_terms: D000465:Algorithms; D019540:Area Under Curve; D002986:Clinical Trials as Topic; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D005189:False Positive Reactions; D006801:Humans; D008175:Lung Neoplasms / Q000175:diagnosis*; D008403:Mass Screening; D015233:Models, Statistical; D015337:Multicenter Studies as Topic; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D013902:Radiography, Thoracic / Q000379:methods*; D003074:Solitary Pulmonary Nodule / Q000175:diagnosis* / Q000473:pathology; D014057:Tomography, X-Ray Computed / Q000379:methods*