Computer-aided detection of ground glass nodules in thoracic CT images using shape, intensity and context features
SourceLecture Notes in Computer Science, 14, Pt 3, (2011), pp. 207-14
Article / Letter to editor
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Lecture Notes in Computer Science
iss. Pt 3
SubjectN4i 3: Poverty-related infectious diseases; ONCOL 5: Aetiology, screening and detection
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.
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