Computer-Aided Detection of Polyps in CT Colonography Using Logistic Regression

—We present a computer-aided detection (CAD) system for computed tomography colonography that orders the polyps according to clinical relevance. The CAD system consists of two steps: candidate detection and supervised classification. The characteristics of the detection step lead to specific choices for the classification system. The candidates are ordered by a linear logistic classifier (logistic regression) based on only three features: the protrusion of the colon wall, the mean internal intensity, and a feature to discard detections on the rectal enema tube. This classifier can cope with a small number of polyps available for training, a large imbalance between polyps and non-polyp candidates, a truncated feature space, unbalanced and unknown misclassification costs, and an exponential distribution with respect to candidate size in feature space. Our CAD system was evaluated with data sets from four different medical centers. For polyps larger than or equal to 6 mm we achiev...

• Type: Journal
• Year: 2010
• Where: TMI
• Authors: Vincent Frans van Ravesteijn, Cees van Wijk, Frans M. Vos, Roel Truyen, J. F. Peters, Jaap Stoker, Lucas J. van Vliet
• Keywords: Detection, Feature Space, Polyps, TMI 2010