Fissure detection methods for lung lobe segmentation
First Claim
Patent Images
1. A method comprising:
- filtering 3D lung image data, the 3D lung image data comprising a plurality of voxels and using at least one of a filter based on both planar structures and vessel suppression,a filter based on curvature computations, and a filter based on local gradient magnitude and direction comparisons;
for each voxel of said plurality of voxels, calculating a score representing the likelihood of the voxel being a fissure using said at least one of a filter based on both planar structures and vessel suppression, a filter based on curvature computations,and a filter based on local gradient magnitude and direction comparisons; and
identifying lobar fissures in the 3D lung image data based on the filtered 3D lung image data resulting from said filtering step.
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Abstract
A fissure detection method for lung lobe segmentation in 3D image data is disclosed. In this method, 3D lung image data is filtered using one or more filters based on at least one of planar structures coupled with vessel suppression, curvature computations, and local gradient magnitude and direction comparisons. Fissures are detected in the 3D lung image data based on the filtered 3D lung image data, and lung lobes are segmented from the 3D lung image data based on the detected fissures.
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Citations
27 Claims
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1. A method comprising:
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filtering 3D lung image data, the 3D lung image data comprising a plurality of voxels and using at least one of a filter based on both planar structures and vessel suppression, a filter based on curvature computations, and a filter based on local gradient magnitude and direction comparisons; for each voxel of said plurality of voxels, calculating a score representing the likelihood of the voxel being a fissure using said at least one of a filter based on both planar structures and vessel suppression, a filter based on curvature computations, and a filter based on local gradient magnitude and direction comparisons; and identifying lobar fissures in the 3D lung image data based on the filtered 3D lung image data resulting from said filtering step. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10)
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11. An apparatus comprising:
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means for filtering 3D lung image data, the 3D lung image data comprising a plurality of voxels and using at least one of a filter based on both planar structures and vessel suppression, a filter based on curvature computations, and a filter based on local gradient magnitude and direction comparisons; mean for calculating a score for each voxel of said plurality of voxels, the score representing the likelihood of the voxel being a fissure using said at least one of a filter based on both planar structures and vessel suppression, a filter based on curvature computations, and a filter based on local gradient magnitude and direction comparisons; and means for identifying lobar fissures in the 3D lung image data based on the filtered 3D lung image data. - View Dependent Claims (12, 13, 14, 15, 16, 17, 18)
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19. A computer readable medium storing computer program instructions, said computer program instructions defining the steps comprising:
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filtering 3D lung image data, the 3D lung image data comprising a plurality of voxels, based on at least one of planar structures coupled with vessel suppression, curvature computations, and local gradient magnitude and direction comparisons; for each voxel of said plurality of voxels, calculating a score representing the likelihood of the voxel being a fissure using said at least one of a filter based on both planar structures and vessel suppression, a filter based on curvature computations, and a filter based on local gradient magnitude and direction comparisons; and identifying lobar fissures in the 3D lung image data based on the filtered 3D lung image data resulting from said filtering step. - View Dependent Claims (20, 21, 22, 23, 24, 25, 26, 27)
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Specification