Computerized method for determination of the likelihood of malignancy for pulmonary nodules on low-dose CT
First Claim
1. A method for determining if a pulmonary nodule is malignant, comprising the steps of:
- obtaining at least one computed tomography medical image of the pulmonary nodule;
obtaining at least one patient feature of a patient having the pulmonary nodule;
extracting image features of the pulmonary nodule from the at least one computed tomography medical image; and
evaluating whether the pulmonary nodule is malignant based on an examination of a total of seven of the patient or image features, wherein the step of obtaining the patient features consists of obtaining the sex of the patient; and
the step of extracting image features consists of extracting effective diameter of the pulmonary nodule, contrast of the pulmonary nodule, overlap measure of two gray-level histograms for the inside and outside regions of a segmented nodule of the medical image, overlap measure of two gray-level histograms for the inside and outside region of a segmented nodule of an edge gradient of the medical image, radial gradient index for an inside region of a segmented nodule of the medical image, and peak value of a histogram for an inside regions of a segmented nodule of an edge gradient of the medical image.
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Accused Products
Abstract
An automated computerized scheme for determination of the likelihood of malignancy in pulmonary nodules. The present invention includes steps of obtaining at least one computed tomography medical image of a pulmonary nodule in determining if the pulmonary nodule is malignant based on the examination of seven patient or image features. The method can be implemented when instructions are loaded into a computer to program the computer. The significance of employing seven patient or image features is that statistically, seven features are the most practical based on the unique implementation of statistical analysis. Out of the seven features that are now analyzed to determine if a pulmonary nodule is malignant, these features are selected to optimize the accuracy of the diagnosis of a pulmonary nodule. Through a unique sampling scheme, different embodiments of the present invention utilize different combinations of features to optimize the accuracy of the method of the present invention.
34 Citations
36 Claims
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1. A method for determining if a pulmonary nodule is malignant, comprising the steps of:
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obtaining at least one computed tomography medical image of the pulmonary nodule;
obtaining at least one patient feature of a patient having the pulmonary nodule;
extracting image features of the pulmonary nodule from the at least one computed tomography medical image; and
evaluating whether the pulmonary nodule is malignant based on an examination of a total of seven of the patient or image features, wherein the step of obtaining the patient features consists of obtaining the sex of the patient; and
the step of extracting image features consists of extracting effective diameter of the pulmonary nodule, contrast of the pulmonary nodule, overlap measure of two gray-level histograms for the inside and outside regions of a segmented nodule of the medical image, overlap measure of two gray-level histograms for the inside and outside region of a segmented nodule of an edge gradient of the medical image, radial gradient index for an inside region of a segmented nodule of the medical image, and peak value of a histogram for an inside regions of a segmented nodule of an edge gradient of the medical image. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10)
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11. A computer readable medium storing computer program instructions for determining if a pulmonary nodule is malignant, which when used to program a computer to cause the computer to perform the steps of:
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obtaining at least one computed tomography medical image of the pulmonary nodule;
obtaining at least one patient feature of a patient having the pulmonary nodule;
extracting image features of the pulmonary nodule from the at least one computed tomography medical image; and
evaluating whether the pulmonary nodule is malignant based on an examination of a total of seven of the patient or image features, wherein the step of obtaining the patient features consists of obtaining the sex of the patient; and
the step of extracting image features consists of extracting effective diameter of the pulmonary nodule, contrast of the pulmonary nodule, overlap measure of two gray-level histograms for the inside and outside regions of a segmented nodule of the medical image, overlap measure of two gray-level histograms for the inside and outside region of a segmented nodule of an edge gradient of the medical image, radial gradient index for an inside region of a segmented nodule of the medical image, and peak value of a histogram for an inside regions of a segmented nodule of an edge gradient of the medical image. - View Dependent Claims (12, 13, 14, 15, 16, 17, 18, 19, 20)
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21. A system for determining if a pulmonary nodule is malignant, comprising:
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a mechanism configured to obtain at least one computed tomography medical image of the pulmonary nodule;
a mechanism configured to obtain at least one patient feature of a patient having the pulmonary nodule;
a mechanism configured to extract image features of the pulmonary nodule from the at least one computed tomography medical image; and
a mechanism configured to evaluate whether the pulmonary nodule is malignant based on an examination of a total of seven of the patient or image features, wherein the mechanism configured to obtain the patient features consists of a mechanism for obtaining the sex of the patient; and
the mechanism configured to extract image features consists of a mechanism configured to extract effective diameter of the pulmonary nodule, contrast of the pulmonary nodule, overlap measure of two gray-level histograms for the inside and outside regions of a segmented nodule of the medical image, overlap measure of two gray-level histograms for the inside and outside region of a segmented nodule of an edge gradient of the medical image, radial gradient index for an inside region of a segmented nodule of the medical image, and peak value of a histogram for an inside regions of a segmented nodule of an edge gradient of the medical image. - View Dependent Claims (22, 23, 24, 25, 26, 27, 28, 29, 30)
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31. A method for determining if a pulmonary nodule is malignant, comprising:
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obtaining at least one computed tomography medical image of the pulmonary nodule;
obtaining at least one patient feature of a patient having the pulmonary nodule;
extracting image features of the pulmonary nodule from the at least one computed tomography medical image; and
evaluating whether the pulmonary nodule is malignant based on an examination of a total of seven of the patient or image features, wherein the step of extracting image features comprises at least one of the steps of identifying image features based on an outline of the at least one computed tomography medical image of the pulmonary nodule;
identifying image features based on linear patterns of the at least one computed tomography medical image of the pulmonary nodule;
identifying image features based on edge orientation of an edge gradient of the at least one computed tomography medical image of the pulmonary nodule;
identifying image features based on gray-level distribution of the at least one computed tomography medical image of the pulmonary nodules;
identifying image features based on the gray level distribution of an edge gradient of the at least one computed tomography medical image of the pulmonary nodule;
identifying image features based on the relationship between two histograms in the inside and outside regions of the segmented nodule of the at least one computed tomography medical image of the pulmonary nodule; and
identifying image features based on the relationship between two histograms in the inside and outside regions of the segmented nodule of an edge gradient of the at least one computed tomography medical image of the pulmonary nodule, and wherein the step of identifying image features based on the linear patterns of the at least one computed tomography medical image of the pulmonary nodule comprises identifying the magnitude of line patterns for inside region of a segmented nodule of the medical image; and
identifying the magnitude of line patterns for outside region of a segmented nodule of the medical image.
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32. A method for determining if a pulmonary nodule is malignant, comprising:
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obtaining at least one computed tomography medical image of the pulmonary nodule;
obtaining at least one patient feature of a patient having the pulmonary nodule;
extracting image features of the pulmonary nodule from the at least one computed tomography medical image; and
evaluating whether the pulmonary nodule is malignant based on an examination of a total of seven of the patient or image features, wherein the step of extracting image features comprises at least one of the steps of identifying image features based on an outline of the at least one computed tomography medical image of the pulmonary nodule;
identifying image features based on linear patterns of the at least one computed tomography medical image of the pulmonary nodule;
identifying image features based on edge orientation of an edge gradient of the at least one computed tomography medical image of the pulmonary nodule;
identifying image features based on gray-level distribution of the at least one computed tomography medical image of the pulmonary nodules;
identifying image features based on the gray level distribution of an edge gradient of the at least one computed tomography medical image of the pulmonary nodule;
identifying image features based on the relationship between two histograms in the inside and outside regions of the segmented nodule of the at least one computed tomography medical image of the pulmonary nodule; and
identifying image features based on the relationship between two histograms in the inside and outside regions of the segmented nodule of an edge gradient of the at least one computed tomography medical image of the pulmonary nodule, and the step of identifying image features based on edge orientation of an edge gradient of the at least one computed tomography medical image of the pulmonary nodule comprises identifying radial gradient index computed by the mean absolute value of a radial edge gradient projected along a radial direction for the inside of a segmented nodule of the medical image;
identifying radial gradient index computed by the mean absolute value of a radial edge gradient projected along a radial direction for the outside of a segmented nodule of the medical image;
identifying tangential gradient index computed by the mean absolute value of a tangential edge gradient projected along a tangential direction for the inside of a segmented nodule of the medical image; and
identifying tangential gradient computed by the mean absolute value of a tangential edge gradient projected along a tangential direction for the outside of a segmented nodule of the image.
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33. A computer readable medium storing computer program instructions for determining if a pulmonary nodule is malignant, which when used to program a computer to cause the computer to perform the steps of:
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obtaining at least one computed tomography medical image of the pulmonary nodule;
obtaining at least one patient feature of a patient having the pulmonary nodule;
extracting image features of the pulmonary nodule from the at least one computed tomography medical image; and
evaluating whether the pulmonary nodule is malignant based on an examination of a total of seven of the patient or image features, wherein the step of extracting image features comprises at least one of the steps of identifying image features based on an outline of the at least one computed tomography medical image of the pulmonary nodule;
identifying image features based on linear patterns of the at least one computed tomography medical image of the pulmonary nodule;
identifying image features based on edge orientation of an edge gradient of the at least one computed tomography medical image of the pulmonary nodule;
identifying image features based on gray-level distribution of the at least one computed tomography medical image of the pulmonary nodules;
identifying image features based on the gray level distribution of an edge gradient of the at least one computed tomography medical image of the pulmonary nodule;
identifying image features based on the relationship between two histograms in the inside and outside regions of the segmented nodule of the at least one computed tomography medical image of the pulmonary nodule; and
identifying image features based on the relationship between two histograms in the inside and outside regions of the segmented nodule of an edge gradient of the at least one computed tomography medical image of the pulmonary nodule, and the step of identifying image features based on the linear patterns of the at least one computed tomography medical image of the pulmonary nodule comprises identifying the magnitude of line patterns for inside region of a segmented nodule of the medical image; and
identifying the magnitude of line patterns for outside region of a segmented nodule of the medical image.
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34. A computer readable medium storing computer program instructions for determining if a pulmonary nodule is malignant, which when used to program a computer to cause the computer to perform the steps of:
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obtaining at least one computed tomography medical image of the pulmonary nodule;
obtaining at least one patient feature of a patient having the pulmonary nodule;
extracting image features of the pulmonary nodule from the at least one computed tomography medical image; and
evaluating whether the pulmonary nodule is malignant based on an examination of a total of seven of the patient or image features, wherein the step of extracting image features comprises at least one of the steps of identifying image features based on an outline of the at least one computed tomography medical image of the pulmonary nodule;
identifying image features based on linear patterns of the at least one computed tomography medical image of the pulmonary nodule;
identifying image features based on edge orientation of an edge gradient of the at least one computed tomography medical image of the pulmonary nodule;
identifying image features based on gray-level distribution of the at least one computed tomography medical image of the pulmonary nodules;
identifying image features based on the gray level distribution of an edge gradient of the at least one computed tomography medical image of the pulmonary nodule;
identifying image features based on the relationship between two histograms in the inside and outside regions of the segmented nodule of the at least one computed tomography medical image of the pulmonary nodule; and
identifying image features based on the relationship between two histograms in the inside and outside regions of the segmented nodule of an edge gradient of the at least one computed tomography medical image of the pulmonary nodule, and the step of identifying image features based on edge orientation of an edge gradient of the at least one computed tomography medical image of the pulmonary nodule comprises identifying radial gradient index computed by the mean absolute value of a radial edge gradient projected along a radial direction for the inside of a segmented nodule of the medical image;
identifying radial gradient index computed by the mean absolute value of a radial edge gradient projected along a radial direction for the outside of a segmented nodule of the medical image;
identifying tangential gradient index computed by the mean absolute value of a tangential edge gradient projected along a tangential direction for the inside of a segmented nodule of the medical image; and
identifying tangential gradient computed by the mean absolute value of a tangential edge gradient projected along a tangential direction for the outside of a segmented nodule of the image.
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35. A system for determining if a pulmonary nodule is malignant, comprising:
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a mechanism configured to obtain at least one computed tomography medical image of the pulmonary nodule;
a mechanism configured to obtain at least one patient feature of a patient having the pulmonary nodule;
a mechanism configured to extract image features of the pulmonary nodule from the at least one computed tomography medical image; and
a mechanism configured to evaluate whether the pulmonary nodule is malignant based on an examination of a total of seven of the patient or image features, wherein the mechanism configured to evaluate image features comprises at least one of a mechanism configured to identify image features based on an outline of the at least one computed tomography medical image of the pulmonary nodule;
a mechanism configured to identify image features based on linear patterns of the at least one computed tomography medical image of the pulmonary nodule;
a mechanism configured to identify image features based on edge orientation of an edge gradient of the at least one computed tomography medical image of the pulmonary nodule;
a mechanism configured to identify image features based on gray-level distribution of the at least one computed tomography medical image of the pulmonary nodules;
a mechanism configured to identify image features based on the gray level distribution of an edge gradient of the at least one computed tomography medical image of the pulmonary nodule;
a mechanism configured to identify image features based on the relationship between two histograms in the inside and outside regions of the segmented nodule of the at least one computed tomography medical image of the pulmonary nodule; and
a mechanism configured identify image features based on the relationship between two histograms in the inside and outside regions of the segmented nodule of an edge gradient of the at least one computed tomography medical image of the pulmonary nodule, wherein the mechanism configured to identify image features based on the linear patterns of the at least one computed tomography medical image of the pulmonary nodule comprises a mechanism configured to identify the magnitude of line patterns for inside region of a segmented nodule of the medical image; and
a mechanism configured to identify the magnitude of line patterns for outside region of a segmented nodule of the medical image.
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36. A system for determining if a pulmonary nodule is malignant, comprising:
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a mechanism configured to obtain at least one computed tomography medical image of the pulmonary nodule;
a mechanism configured to obtain at least one patient feature of a patient having the pulmonary nodule;
a mechanism configured to extract image features of the pulmonary nodule from the at least one computed tomography medical image; and
a mechanism configured to evaluate whether the pulmonary nodule is malignant based on an examination of a total of seven of the patient or image features, wherein the mechanism configured to identify image features comprises at least one of a mechanism configured to identify image features based on an outline of the at least one computed tomography medical image of the pulmonary nodule;
a mechanism configured to identify image features based on linear patterns of the at least one computed tomography medical image of the pulmonary nodule;
a mechanism configured to identify image features based on edge orientation of an edge gradient of the at least one computed tomography medical image of the pulmonary nodule;
a mechanism configured to identify image features based on gray-level distribution of the at least one computed tomography medical image of the pulmonary nodules;
a mechanism configured to identify image features based on the gray level distribution of an edge gradient of the at least one computed tomography medical image of the pulmonary nodule;
a mechanism configured to identify image features based on the relationship between two histograms in the inside and outside regions of the segmented nodule of the at least one computed tomography medical image of the pulmonary nodule; and
a mechanism configured to identify image features based on the relationship between two histograms in the inside and outside regions of the segmented nodule of an edge gradient of the at least one computed tomography medical image of the pulmonary nodule, wherein the mechanism configured to identify image features based on edge orientation of an edge gradient of the at least one computed tomography medical image of the pulmonary nodule comprises a mechanism configured to identify radial gradient index computed by the mean absolute value of a radial edge gradient projected along a radial direction for the inside of a segmented nodule of the medical image;
a mechanism configured to identify radial gradient index computed by the mean absolute value of a radial edge gradient projected along a radial direction for the outside of a segmented nodule of the medical image;
a mechanism configure to identify tangential gradient index computed by the mean absolute value of a tangential edge gradient projected along a tangential direction for the inside of a segmented nodule of the medical image; and
a mechanism configured to identify tangential gradient computed by the mean absolute value of a tangential edge gradient projected along a tangential direction for the outside of a segmented nodule of the image.
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Specification