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Computer-aided detection of ground glass nodules in thoracic CT images using shape, intensity and context features

: Jacobs, C.; Sánchez, C.I.; Saur, S.C.; Twellmann, T.; Jong, P.A. de; Ginneken, B. van


Fichtinger, G.:
Medical image computing and computer-assisted intervention, MICCAI 2011. 14th international conference. Pt.3 : Toronto, Canada, September 18-22, 2011; proceedings
Berlin: Springer, 2011 (Lecture Notes in Computer Science 6893)
ISBN: 3-642-23625-1
ISBN: 978-3-642-23625-9
ISBN: 978-3-642-23626-6
ISSN: 0302-9743
International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI) <14, 2011, Toronto>
Conference Paper
Fraunhofer MEVIS ()

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.