Abstract
Colorectal cancer can easily be prevented provided that the precursors to tumors, small colonic polyps, are detected and removed. Currently, the only definitive examination of the colon is fiber-optic colonoscopy, which is invasive and expensive. Computed tomographic colonography (CTC) is potentially a less costly and less invasive alternative to FOC. It would be desirable to have computer-aided detection (CAD) algorithms to examine the large amount of data CTC provides. Most current CAD algorithms have high false positive rates at the required sensitivity levels. We developed and evaluated a postprocessing algorithm to decrease the false positive rate of such a CAD method without sacrificing sensitivity. Our method attempts to model the way a radiologist recognizes a polyp while scrolling a cross-sectional plane through three-dimensional computed tomography data by classification of the changes in the location of the edges in the two-dimensional plane. We performed a tenfold cross-validation study to assess its performance using sensitivity/specificity analysis on data from 48 patients. The mean specificity over all experiments increased from 0.19 (0.35) to 0.47 (0.56) for a sensitivity of 1.00 (0.95).
