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A modified gradient correlation filter for image segmentation: Application to airway and bowel
Author(s) -
Sensakovic William F.,
Starkey Adam,
Armato Samuel G.
Publication year - 2009
Publication title -
medical physics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.473
H-Index - 180
eISSN - 2473-4209
pISSN - 0094-2405
DOI - 10.1118/1.3056461
Subject(s) - medical imaging , airway , medicine , correlation , image segmentation , filter (signal processing) , image processing , computer vision , artificial intelligence , image (mathematics) , radiology , mathematics , computer science , surgery , geometry
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.