title: A modified gradient correlation filter for image segmentation: application to airway and bowel.
abstract: The segmentation of structures of interest from medical images may incorrectly include adjacent structures in the segmented image (i.e., false positives). This study introduces a family of gradient correlation filters that reduce false positives in the segmented image by comparing the segmented region gradients with a user-defined model. A gradient correlation filter was applied to a database of clinical computed tomography scans for the task of differentiating airway from lung regions and bowel from lung regions. The results were evaluated using receiver-operating characteristic analysis and demonstrated excellent results for both the airway/lung and bowel/lung classification tasks.
authors.affiliation: Department of Radiology, The University of Chicago, 5841 South Maryland Avenue, Chicago, Illinois 60637, USA. wfsensak@uchicago.edu
authors.forename: William F
authors.initials: WF
authors.lastname: Sensakovic
authors.forename: Adam
authors.initials: A
authors.lastname: Starkey
authors.forename: Samuel G
authors.initials: SG
authors.lastname: Armato
mesh_terms: D000368:Aged; D003952:Diagnostic Imaging / Q000379:methods*; D005189:False Positive Reactions; D005260:Female; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D007422:Intestines; D008168:Lung; D008297:Male; D012372:ROC Curve; D014057:Tomography, X-Ray Computed