Automated detection of lung nodules from multi-slice CT image data
First Claim
1. A method for detecting features of predetermined size and shape in an image comprising the steps of:
- identifying at least a first border of an object in said image, said border being defined by a plurality of points defined by a plurality of pixels in said image;
calculating a curvature value for said border at each of said points;
identifying a set of high curvature points selected from said plurality of points where said border has a curvature value greater than a threshold value; and
generating a set of regions in said image, each of which represents a potential feature of said predetermined size and shape, by analyzing pairs of said high curvature points to determine whether the points in each pair potentially define a region representing one of said features.
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Accused Products
Abstract
An automated method and system for detecting lung nodules from thoracic CT images employs an image processing algorithm (22) consisting of two main modules: a detection module (24) that detects nodule candidates from a given lung CT image dataset, and a classifier module (26), which classifies the nodule candidates as either true or false to reject false positives amongst the candidates. The detection module (24) employs a curvature analysis technique, preferably based on a polynomial fit, that enables accurate calculation of lung border curvature to facilitate identification of juxta-pleural lung nodule candidates, while the classification module (26) employs a minimal number of image features (e.g., 3) in conjunction with a Bayesian classifier to identify false positives among the candidates.
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Citations
40 Claims
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1. A method for detecting features of predetermined size and shape in an image comprising the steps of:
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identifying at least a first border of an object in said image, said border being defined by a plurality of points defined by a plurality of pixels in said image;
calculating a curvature value for said border at each of said points;
identifying a set of high curvature points selected from said plurality of points where said border has a curvature value greater than a threshold value; and
generating a set of regions in said image, each of which represents a potential feature of said predetermined size and shape, by analyzing pairs of said high curvature points to determine whether the points in each pair potentially define a region representing one of said features. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14)
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15. A method for detecting features of predetermined size and shape along a border of an object in an image comprising the steps of:
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generating a set of regions in said image corresponding to potential features of said predetermined size and shape; and
classifying each region is said set of regions as being either true or false, where true defines a first subset of said regions that actually represent features of said predetermined size and shape, and false defines a second subset of said regions that do not represent features of said predetermined size and shape. - View Dependent Claims (16, 17, 18, 19, 20)
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21. A system for detecting features of predetermined size and shape in an image comprising:
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an image acquisition system for generating one or more images; and
a computer including a memory for storing said images received from said image acquisition system and a processor for analyzing said images that is programmed with an algorithm that carries out the steps of;
identifying at least a first border of an object in each of said images, said border being defined by a plurality of points defined by a plurality of pixels in said image;
calculating a curvature value for said border at each of said points;
identifying a set of high curvature points selected from said plurality of points where said border has a curvature value greater than a threshold value; and
generating a set of regions in each said image, each of which represents a potential feature of said predetermined size and shape, by analyzing pairs of said high curvature points to determine whether the points in each pair potentially define a region representing one of said features. - View Dependent Claims (22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34)
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35. A system for detecting features of predetermined size and shape along a border of an object in an image comprising:
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an image acquisition system for generating one or more images; and
a computer including a memory for storing said images received from said image acquisition system and a processor for analyzing said images that is programmed with an algorithm that carries out the steps of;
generating a set of regions in each said image corresponding to potential features of said predetermined size and shape; and
classifying each region is said set of regions as being either true or false, where true defines a first subset of said regions that actually represent features of said predetermined size and shape, and false defines a second subset of said regions that do not represent features of said predetermined size and shape. - View Dependent Claims (36, 37, 38, 39, 40)
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Specification