Automated method and system for the detection of lesions in medical computed tomographic scans
First Claim
1. A method for the automated detection of nodules in a computed tomographic (CT) scan of a subject, comprising:
- detecting an anatomic region of said subject in said CT scan; and
detecting a nodule in said anatomic region using a plurality of images of said anatomic region in said CT scan generated at different section-specific gray-level thresholds.
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Abstract
A method and system for the automated detection of lesions in computed tomographic images, including generating image data from at least one selected portion of an object, for example, from CT images of the thorax. The image data are then analyzed in order to produce the boundary of the thorax. The image data within the thoracic boundary is then further analyzed to produce boundaries of the lung regions using predetermined criteria. Features within the lung regions are then extracted using multi-gray-level thresholding and correlation between resulting multi-level threshold images and between at least adjacent sections. Classification of the features as abnormal lesions or normal anatomic features is then performed using geometric features yielding a likelihood of being an abnormal lesion along with its location in either the 2-D image section or in the 3-D space of the object.
163 Citations
43 Claims
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1. A method for the automated detection of nodules in a computed tomographic (CT) scan of a subject, comprising:
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detecting an anatomic region of said subject in said CT scan; and detecting a nodule in said anatomic region using a plurality of images of said anatomic region in said CT scan generated at different section-specific gray-level thresholds. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33)
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34. A system for the automated detection of lesions in computed tomographic (CT) scan, comprising;
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an image generation device to generate said CT scan of an anatomic region; a device for generating gray-level images of said CT scan at a plurality of gray-level thresholds; and a device for analyzing features in said gray-level images; wherein said device for generating gray-level threshold images comprises; a histogram generator which generates a histogram of gray-values of pixels in said CT scan; a threshold generator for determining a plurality of gray-level thresholds using said histogram; and a binary image generator for generating a respective plurality of binary images of said anatomic region using said gray-level thresholds; said CT scan comprising a plurality of CT sections; said binary image generator generating a respective plurality of binary images for each of said CT sections; and a tree structure generator producing a tree structure of features in said respective plurality of binary images for each CT section. - View Dependent Claims (35, 36)
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37. A method for the automated detection of nodules in a computed tomographic (CT) scan of a subject, comprising:
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detecting an anatomic region of said subject in said CT scan; detecting a nodule in said anatomic region using a plurality of images of said anatomic region in said CT scan generated at different gray-level thresholds; detecting features in said anatomic region as suspected nodules using said plurality of images; determining geometric descriptors for selected ones of said features; and detecting said nodule using said geometric descriptors; wherein said step of determining geometric descriptors comprises determining plural of perimeter, area, compactness, elongation, circularity, distance measure and total score for said selected ones of said features.
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38. A method for the automated detection of nodules in a computed tomographic (CT) scan of a subject, comprising:
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detecting an anatomic region of said subject in said CT scan; detecting a nodule in said anatomic region using a plurality of images of said anatomic region in said CT scan generated at different gray-level thresholds; detecting features in said anatomic region as suspected nodules using said plurality of images; determining geometric descriptors for selected ones of said features; and detecting said nodule using said geometric descriptors; wherein said step of detecting said nodule using said geometric descriptors comprises assigning each of said features a likelihood of being a nodule.
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39. A method for the automated detection of nodules in a computed tomographic (CT) scan of a subject, comprising:
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detecting an anatomic region of said subject in said CT scan; detecting a nodule in said anatomic region using a plurality of images of said anatomic region in said CT scan generated at different gray-level thresholds; detecting features in said anatomic region as suspected nodules using said plurality of images; and performing a corner correction routine on selected ones of said features.
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40. A method for the automated detection of nodules in a computed tomographic (CT) scan of a subject, comprising:
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detecting an anatomic region of said subject in said CT scan; detecting a nodule in said anatomic region using a plurality of images of said anatomic region in said CT scan generated at different gray-level thresholds; determining features in anatomic region using said images; and forming a tree structure of features in said binary images. - View Dependent Claims (41, 42, 43)
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Specification