Automated method and system for the detection of gross abnormalities and asymmetries in chest images
First Claim
1. A method for detecting an abnormality in a radiographic image, comprising:
- obtaining a radiographic image of a subject;
detecting a boundary of a feature in said radiographic image;
determining geometric descriptors of said feature; and
determining whether an abnormality exists using said geometric descriptors;
wherein said step of detecting said boundary of said feature comprises;
performing iterative global gray-level thresholding on said radiographic image, comprising,determining a plurality of gray-level theshold valuesiteratively creating respective binary images defining at least one region based on each of said plurality of said gray-level threshold values,defining a characteristic limit for said at least one region,eliminating said at least one region from the respective image and subsequent images iteratively obtained if said at least one region has a characteristic outside said characteristic limit, anddetermining a final region resulting after iterative eliminating of regions and iterative thresholding at said plurality of gray-level threshold values as said feature.
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Abstract
A method for the automated detection of gross abnormalities and asymmetries in chest images, including generating image data from radiographic images of the thorax. The image data are then analyzed in order to produce the boundaries of the aerated lung regions in the thorax. This analysis comprises location of the mediastinum and lung apices, iterative global thresholding with centroid testing of contours, local thresholding on regions along initial contours of the aerated lung, correction for regions near the costo- and cardiophrenic angles in the chest, analysis of the areas and density distribution within the aerated lung regions in the chest and determination of the likelihood of the presence of a gross abnormality or asymmetry. Final output could be the computer determined contours of the lungs or the likelihood for abnormality.
84 Citations
48 Claims
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1. A method for detecting an abnormality in a radiographic image, comprising:
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obtaining a radiographic image of a subject; detecting a boundary of a feature in said radiographic image; determining geometric descriptors of said feature; and determining whether an abnormality exists using said geometric descriptors; wherein said step of detecting said boundary of said feature comprises; performing iterative global gray-level thresholding on said radiographic image, comprising, determining a plurality of gray-level theshold values iteratively creating respective binary images defining at least one region based on each of said plurality of said gray-level threshold values, defining a characteristic limit for said at least one region, eliminating said at least one region from the respective image and subsequent images iteratively obtained if said at least one region has a characteristic outside said characteristic limit, and determining a final region resulting after iterative eliminating of regions and iterative thresholding at said plurality of gray-level threshold values as said feature. - View Dependent Claims (2, 3, 4, 5, 6, 14, 15)
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7. A method as recited in 6, wherein performing local gray-level thresholding comprises:
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placing regions-of-interest having a plurality of pixels along said initial boundary; determining a second gray-level threshold value for each of said regions-of-interest; selecting pixels in said regions-of-interest based upon respective ones of said second gray-level threshold values to obtain selected pixels; and forming a second binary image based upon said selected pixels. - View Dependent Claims (8, 9, 10, 11, 12, 13)
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16. A method for detecting an abnormality in a radiographic chest image, comprising:
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obtaining a radiographic chest image of a subject; detecting a boundary of lungs in said radiographic image; determining geometric descriptors of said lungs; determining whether an abnormality exists in said lungs using said geometric descriptors; selectively positioning a first region-of-interest over the thorax in said radiographic chest image; constructing a gray-level histogram using pixels in said first region-of-interest; performing a running slope of said gray-level histogram to construct a smoothed gray-level histogram; and determining a number of first gray-level threshold values at selected locations in said smoothed gray-level histogram. - View Dependent Claims (17, 18, 19)
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20. A method as recited in 19, wherein performing local gray-level thresholding comprises:
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placing regions-of-interest having a plurality of pixels along said initial boundary; determining a second gray-level threshold value for each said region-of-interest; selecting pixels in said regions-of-interest based upon respective ones of said second gray-level threshold values to obtain selected pixels; and forming a second binary image based upon said selected pixels. - View Dependent Claims (21, 22, 23, 24, 25, 26, 27, 28, 29, 30)
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31. A system for detecting an abnormality in a radiographic image, comprising:
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an image acquisition device; an image memory connected to said image acquisition device; an iterative global thresholding circuit connected to said image memory; a centroid circuit connected to said iterative global threshold circuit; a local thresholding circuit connected to said centroid circuit; a correction circuit connected to said local thresholding circuit; a sizing circuit connected to said correction circuit; a density circuit connected to said correction circuit; a comparison circuit connected to said sizing and density circuits; and a display connected to receive an input of said comparison circuit. - View Dependent Claims (32, 33, 34, 35, 36, 37)
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38. A method for automated analysis of a radiographic image, comprising:
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obtaining a radiographic image of a subject; and detecting a boundary of a feature in said radiographic image by performing iterative global gray-level thresholding on said radiographic image, comprising, determining a plurality of gray-level threshold values iteratively creating respective binary images defining at least one region based on each of said plurality of said gray-level threshold values, defining a characteristic limit for said at least one region, eliminating said at least one region from the respective image and subsequent images iteratively obtained if said at least one region has a characteristic outside said characteristic limit, and determining a final region resulting after iterative eliminating of regions and iterative thresholding at said plurality of gray level threshold values as said feature. - View Dependent Claims (39, 40, 41, 42, 43, 44, 45, 46, 47, 48)
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Specification