title: A CAD of fully automated colonic polyp detection for contrasted and non-contrasted CT scans.
abstract: OBJECTIVE
  Computer-aided detection (CAD) systems are developed to help radiologists detect colonic polyps over CT scans. It is possible to reduce the detection time and increase the detection accuracy rates by using CAD systems. In this paper, we aimed to develop a fully integrated CAD system for automated detection of polyps that yields a high polyp detection rate with a reasonable number of false positives.
  
  
  METHODS
  The proposed CAD system is a multistage implementation whose main components are: automatic colon segmentation, candidate detection, feature extraction and classification. The first element of the algorithm includes a discrete segmentation for both air and fluid regions. Colon-air regions were determined based on adaptive thresholding, and the volume/length measure was used to detect air regions. To extract the colon-fluid regions, a rule-based connectivity test was used to detect the regions belong to the colon. Potential polyp candidates were detected based on the 3D Laplacian of Gaussian filter. The geometrical features were used to reduce false-positive detections. A 2D projection image was generated to extract discriminative features as the inputs of an artificial neural network classifier.
  
  
  RESULTS
  Our CAD system performs at 100% sensitivity for polyps larger than 9 mm, 95.83% sensitivity for polyps 6-10 mm and 85.71% sensitivity for polyps smaller than 6 mm with 5.3 false positives per dataset. Also, clinically relevant polyps ([Formula: see text]6 mm) were identified with 96.67% sensitivity at 1.12 FP/dataset.
  
  
  CONCLUSIONS
  To the best of our knowledge, the novel polyp candidate detection system which determines polyp candidates with LoG filters is one of the main contributions. We also propose a new 2D projection image calculation scheme to determine the distinctive features. We believe that our CAD system is highly effective for assisting radiologist interpreting CT.
authors.affiliation: Electrical and Electronics Engineering, Istanbul Yeni Yuzyil University Istanbul, Istanbul, Turkey. gokalp.tulum@yeniyuzyil.edu.tr.
authors.forename: Gökalp
authors.identifier: http://orcid.org/0000-0003-2865-5781
authors.initials: G
authors.lastname: Tulum
authors.affiliation: Electronics and Communication Engineering, Yildiz Technical University Istanbul, Istanbul, Turkey.
authors.forename: Bülent
authors.initials: B
authors.lastname: Bolat
authors.affiliation: Electrical and Electronics Engineering, Istanbul Arel University, Istanbul, Turkey.
authors.forename: Onur
authors.initials: O
authors.lastname: Osman
mesh_terms: D000465:Algorithms; D003106:Colon / Q000000981:diagnostic imaging; D003111:Colonic Polyps / Q000000981:diagnostic imaging*; D023881:Colonography, Computed Tomographic / Q000379:methods*; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D006801:Humans; D010363:Pattern Recognition, Automated / Q000379:methods; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D012680:Sensitivity and Specificity
keywords: Colon segmentation; Computed tomography images; Computer-aided detection; Polyp detection