A new high-performance CAD scheme for the detection of polyps in CT colonography

We developed a new method for automated detection of colonic polyps in CT colonography. The colon is extracted from CT images by use of a centerline-based colon segmentation method. Polyp candidates are detected by use of hysteresis thresholding and fuzzy merging. The regions of the polyp candidates are segmented by use of conditional morphological dilation. False-positive polyp candidates are reduced by a region-based supine-prone correspondence method and by a Bayesian neural network with shape and texture features. To evaluate the method. CT colonography was performed for 121 patients with standard technique and single- and multi-detector helical scanners by use of 2.5 - 5.0 mm collimations, 1.0 - 5.0 mm reconstruction intervals, and 60 - 100 mA tube currents. Twenty-eight patients had a total of 42 polyps: 22 polyps were 5 - 10 mm, and 20 polyps were 11 - 25 mm in size. A leave-one-out evaluation of the CAD scheme with by-patient elimination yielded 93% by-polyp and by-patient detection sensitivities with 2.0 false-positive detections per data set on average. The average computation time was 4 minutes per data set. The results indicate that the CAD scheme may be useful in improving the performance of computer-aided detection for colon cancer in a clinical screening setting.