Computer aided diagnostic system incorporating appearance analysis for diagnosing malignant lung nodules
First Claim
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1. A method of classifying a pulmonary nodule, the method comprising:
- receiving image data associated with a chest scan;
segmenting image data associated with lung tissue from the image data associated with the chest scan;
equalizing the segmented image data;
segmenting image data associated with a pulmonary nodule from the equalized and segmented image data; and
classifying the pulmonary nodule as benign or malignant by applying a learned appearance model to pulmonary nodule image data for the pulmonary nodule from the segmented image data associated with the pulmonary nodule, wherein the learned appearance model is based upon visual appearances of a plurality of known pulmonary nodules and models voxel-wise conditional Gibbs energies, wherein classifying the pulmonary nodule as benign or malignant includes determining an intensity variation between voxels within the pulmonary nodule image data, and classifying the pulmonary nodule as benign or malignant based at least in part on the determined intensity variation.
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Abstract
A computer aided diagnostic system and automated method diagnose lung cancer through modeling and analyzing the visual appearance of pulmonary nodules. A learned appearance model used in such analysis describes the appearance of pulmonary nodules in terms of voxel-wise conditional Gibbs energies for a generic rotation and translation invariant second-order Markov-Gibbs random field (MGRF) model of malignant nodules with analytically estimated characteristic voxel neighborhoods and potentials.
19 Citations
15 Claims
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1. A method of classifying a pulmonary nodule, the method comprising:
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receiving image data associated with a chest scan; segmenting image data associated with lung tissue from the image data associated with the chest scan; equalizing the segmented image data; segmenting image data associated with a pulmonary nodule from the equalized and segmented image data; and classifying the pulmonary nodule as benign or malignant by applying a learned appearance model to pulmonary nodule image data for the pulmonary nodule from the segmented image data associated with the pulmonary nodule, wherein the learned appearance model is based upon visual appearances of a plurality of known pulmonary nodules and models voxel-wise conditional Gibbs energies, wherein classifying the pulmonary nodule as benign or malignant includes determining an intensity variation between voxels within the pulmonary nodule image data, and classifying the pulmonary nodule as benign or malignant based at least in part on the determined intensity variation. - View Dependent Claims (2, 3, 4, 5, 6, 7)
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8. An apparatus, comprising:
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at least one processor; and program code configured to be executed by the at least one processor to classify a pulmonary nodule as benign or malignant by applying a learned appearance model to pulmonary nodule image data for the pulmonary nodule, wherein the learned appearance model is based upon visual appearances of a plurality of known pulmonary nodules and models voxel-wise conditional Gibbs energies, wherein the program code is further configured to determine intensity variation between voxels within the pulmonary nodule image data, and wherein the program code is configured to classify the pulmonary nodule as benign or malignant based at least in part on the determined intensity variation.
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9. A program product, comprising:
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a non-transitory computer readable storage medium; and program code stored on the non-transitory computer readable storage medium and configured upon execution to classify a pulmonary nodule as benign or malignant by applying a learned appearance model to pulmonary nodule image data for the pulmonary nodule, wherein the learned appearance model is based upon visual appearances of a plurality of known pulmonary nodules and models voxel-wise conditional Gibbs energies, wherein the program code is further configured to determine intensity variation between voxels within the pulmonary nodule image data, and wherein the program code is configured to classify the pulmonary nodule as benign or malignant based at least in part on the determined intensity variation.
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10. A method of generating a learned appearance model for classifying pulmonary nodules as benign or malignant, comprising:
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normalizing image data associated with a plurality of known pulmonary nodules; and processing the normalized image data to learn a 3D appearance of the plurality of known pulmonary nodules to generate the learned appearance model, wherein the learned appearance model comprises an energy probability model that models voxel-wise conditional Gibbs energies of a plurality of known benign and malignant pulmonary nodules for use in classifying unknown pulmonary nodules as benign or malignant. - View Dependent Claims (11, 12, 13, 14, 15)
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Specification