title: Improved curvature estimation for computer-aided detection of colonic polyps in CT colonography.
abstract: OBJECTIVE
  Current schemes for computer-aided detection (CAD) of colon polyps usually use kernel methods to perform curvature-based shape analysis. However, kernel methods may deliver spurious curvature estimations if the kernel contains two surfaces, because of the vanished gradient magnitudes. The aim of this study was to use the Knutsson mapping method to deal with the difficulty of providing better curvature estimations and to assess the impact of improved curvature estimation on the performance of CAD schemes.
  
  
  METHODS
  The new method was compared to two widely used kernel methods in terms of the performance of two stages of CAD: initial detection and true-positive and false-positive classification. The evaluation was conducted on a database of 130 computed tomographic scans from 67 patients. In these patient scans, there were 104 clinically significant polyps and masses >5 mm.
  
  
  RESULTS
  In the initial detection stage, the detection sensitivity of the three methods was comparable. In the classification stage, at a 90% sensitivity level on the basis of the input of this step, the new technique yielded 3.15 false-positive results per scan, demonstrating reductions in false-positive findings of 30.2% (P < .01) and 27.9% (P < .01) compared to the two kernel methods.
  
  
  CONCLUSIONS
  The new method can benefit CAD schemes with reduced false-positive rates, without sacrificing detection sensitivity.
authors.affiliation: Department of Radiology, State University of New York, Stony Brook, NY 11794, USA.
authors.forename: Hongbin
authors.initials: H
authors.lastname: Zhu
authors.forename: Yi
authors.initials: Y
authors.lastname: Fan
authors.forename: Hongbing
authors.initials: H
authors.lastname: Lu
authors.forename: Zhengrong
authors.initials: Z
authors.lastname: Liang
mesh_terms: D003111:Colonic Polyps / Q000000981:diagnostic imaging*; D023881:Colonography, Computed Tomographic / Q000379:methods*; D006801:Humans; D021621:Imaging, Three-Dimensional / Q000379:methods; D010363:Pattern Recognition, Automated / Q000379:methods*; D019047:Phantoms, Imaging; D011237:Predictive Value of Tests; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity