Method, system and computer readable medium for the two-dimensional and three-dimensional detection of lesions in computed tomography scans
First Claim
1. A method for automated detection of lung nodules in computed tomography (CT) image scans, comprising:
- generating two-dimensional segmented lung images by segmenting a plurality of two-dimensional CT image sections derived from said CT image scans;
generating three-dimensional segmented lung volume images by combining said two-dimensional segmented lung images;
determining three-dimensional lung nodule candidates from said three-dimensional segmented lung volume images, including, identifying structures within said three-dimensional segmented lung volume images that meet a volume criterion;
deriving features from said lung nodule candidates; and
detecting lung nodules by analyzing said features to eliminate false-positive nodule candidates from said nodule candidates.
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Abstract
A method, system and computer readable medium for automated detection of lung nodules in computed tomography (CT) image scans, including generating two-dimensional segmented lung images by segmenting a plurality of two-dimensional CT image sections derived from the CT image scans; generating three-dimensional segmented lung volume images by combining the two-dimensional segmented lung images; determining three-dimensional lung nodule candidates from the three-dimensional segmented lung volume images, including, identifying structures within the three-dimensional segmented lung volume images that meet a volume criterion; deriving features from the lung nodule candidates; and detecting lung nodules by analyzing the features to eliminate false-positive nodule candidates from the nodule candidates.
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Citations
37 Claims
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1. A method for automated detection of lung nodules in computed tomography (CT) image scans, comprising:
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generating two-dimensional segmented lung images by segmenting a plurality of two-dimensional CT image sections derived from said CT image scans;
generating three-dimensional segmented lung volume images by combining said two-dimensional segmented lung images;
determining three-dimensional lung nodule candidates from said three-dimensional segmented lung volume images, including, identifying structures within said three-dimensional segmented lung volume images that meet a volume criterion;
deriving features from said lung nodule candidates; and
detecting lung nodules by analyzing said features to eliminate false-positive nodule candidates from said nodule candidates. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 36, 37)
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16. A method for automated segmentation of lung regions from computed tomography (CT) image scans, comprising:
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generating two-dimensional segmented lung images by segmenting a plurality of two-dimensional CT image sections derived from said CT image scans; and
generating three-dimensional segmented lung volume images by combining said two-dimensional segmented lung images;
wherein said step of generating said segmented lung images comprises the steps of;
generating two-dimensional segmented thorax images by segmenting said plurality of two-dimensional CT image sections, including, applying gray-level thresholds to said CT image sections to determine thorax region contours therein; and
generating said two-dimensional segmented lung images by segmenting said segmented thorax images, including, applying gray-level thresholds to said segmented thorax images to determine said lung region contours therein. - View Dependent Claims (17, 18, 19)
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20. A method for automated segmentation of lung nodules in computed tomography (CT) image scans, comprising:
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generating two-dimensional segmented lung images by segmenting a plurality of two-dimensional CT image sections derived from said CT image scans;
generating three-dimensional segmented lung volume images by combining said two-dimensional segmented lung images; and
determining three-dimensional lung nodule candidates from said three-dimensional segmented lung volume images, including, identifying structures within said three-dimensional segmented lung volume images that meet a volume criterion;
wherein said step of generating said segmented lung volume images comprises;
generating said segmented lung images at a plurality of gray levels thresholds; and
combining said segmented lung images to generate segmented lung volume images at a plurality of gray levels corresponding to said grey level thresholds. - View Dependent Claims (21, 22, 23)
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24. A method for automated detection of lung nodules in computed tomography (CT) image scans, comprising:
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generating two-dimensional segmented lung images by segmenting a plurality of two-dimensional CT image sections derived from said CT image scans;
generating three-dimensional segmented lung volume images by combining said two-dimensional segmented lung images;
determining three-dimensional lung nodule candidates from said three-dimensional segmented lung volume images, including, identifying structures within said three-dimensional segmented lung volume images that meet a volume criterion;
deriving features from said lung nodule candidates; and
detecting lung nodules by analyzing said features to eliminate false-positive nodule candidates from said nodule candidates;
wherein said step of deriving said features from said lung nodule candidates comprises;
applying radial gradient index analysis in two or three dimensions on said identified structures to identify false-positive nodule candidates; and
said step of detecting said lung nodules comprises analyzing said radial gradient index to eliminate said false-positive nodule candidates from said nodule candidates. - View Dependent Claims (25, 26, 27)
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28. A method for automated detection of lung nodules in computed tomography (CT) image scans, comprising:
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generating two-dimensional segmented lung images by segmenting a plurality of two-dimensional CT image sections derived from said CT image scans;
generating three-dimensional segmented lung volume images by combining said two-dimensional segmented lung images;
determining three-dimensional lung nodule candidates from said three-dimensional segmented lung volume images, including, identifying structures within said three-dimensional segmented lung volume images that meet a volume criterion;
deriving features from said lung nodule candidates; and
detecting lung nodules by analyzing said features to eliminate false-positive nodule candidates from said nodule candidates;
wherein said step of deriving said features from said lung nodule candidates comprises;
applying similarity index analysis in two or three dimensions on said identified structures to compute a size of a nodule candidate relative to a distribution of sizes for neighboring nodule candidates; and
said step of detecting said lung nodules comprises analyzing said similarity index to eliminate said false-positive nodule candidates from said nodule candidates. - View Dependent Claims (29, 30, 31)
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32. A method for automated analysis of features of lung nodules in computed tomography (CT) image scans, comprising:
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generating two-dimensional segmented lung images by segmenting a plurality of two-dimensional CT image sections derived from said CT image scans;
generating three-dimensional segmented lung volume images by combining said two-dimensional segmented lung images;
determining three-dimensional lung nodule candidates from said three-dimensional segmented lung volume images, including, identifying structures within said three-dimensional segmented lung volume images that meet a volume criterion; and
deriving features from said lung nodule candidates;
wherein said step of deriving said features from said lung nodule candidates comprises;
determining features from said nodule candidates including at least one of structure volume, sphericity, radius of equivalent sphere, maximum compactness, maximum circularity, maximum eccentricity, mean gray level within structure, standard deviation of gray level and gray-level threshold at which structure volume first decreases below an upper volume bound. - View Dependent Claims (33, 34, 35)
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Specification