A modified gradient correlation filter for image segmentation: application to airway and bowel

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.