Automated method and system for the alignment and correlation of images from two different modalities
First Claim
1. A method of correlating images from two modalities, comprising:
- obtaining a first image containing an anatomic feature using a first modality;
obtaining a second image containing said anatomic feature using a second modality;
detecting a first feature contour of said anatomic feature in said first image iteratively using estimated contours derived using a plurality of successive gray-level thresholding operations at a respective plurality of gray-level thresholds, including;
thresholding said first image using one of said plurality of gray-level thresholds,determining whether to alter pixels in said first image, andthresholding said first image using a subsequent one of said gray-level thresholds after said determining step;
detecting a second feature contour of said anatomic feature in said second image; and
superposing said first feature contour and said second feature contour.
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Abstract
A method and system for the computerized registration of radionuclide images with radiographic images, including generating image data from radiographic and radionuclide images of the thorax. Techniques include contouring the lung regions in each type of chest image, scaling and registration of the contours based on location of lung apices, and superimposition after appropriate shifting of the images. Specific applications are given for the automated registration of radionuclide lungs scans with chest radiographs. The method in the example given yields a system that spatially registers and correlates digitized chest radiographs with V/Q scans in order to correlate V/Q functional information with the greater structural detail of chest radiographs. Final output could be the computer-determined contours from each type of image superimposed on any of the original images, or superimposition of the radionuclide image data, which contains high activity, onto the radiographic chest image.
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Citations
52 Claims
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1. A method of correlating images from two modalities, comprising:
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obtaining a first image containing an anatomic feature using a first modality; obtaining a second image containing said anatomic feature using a second modality; detecting a first feature contour of said anatomic feature in said first image iteratively using estimated contours derived using a plurality of successive gray-level thresholding operations at a respective plurality of gray-level thresholds, including; thresholding said first image using one of said plurality of gray-level thresholds, determining whether to alter pixels in said first image, and thresholding said first image using a subsequent one of said gray-level thresholds after said determining step; detecting a second feature contour of said anatomic feature in said second image; and superposing said first feature contour and said second feature contour. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13)
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14. A method of correlating images from two modalities, comprising:
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obtaining a first image containing an anatomic feature using a first modality; obtaining a second image containing said anatomic feature using a second modality; detecting a first feature contour of said anatomic feature in said first image using iterative gray-level thresholding; detecting a second feature contour of said anatomic feature in said second image; and superposing said first feature contour and said second feature contour; wherein performing iterative gray-level thresholding comprises obtaining a global gray-level histogram of said first image including said anatomic feature; obtaining a first derivative of said global gray-level histogram to identify a peak and a minimum in said histogram; determining gray-level values of said peak and minimum; and selecting a predetermined number of gray-level values spaced between gray-level values of said peak and said minimum as said global gray-level threshold values.
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15. A method of correlating images from two modalities, comprising:
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obtaining a first image containing an anatomic feature using a first modality; obtaining a second image containing said anatomic feature using a second modality; detecting a first feature contour of said anatomic feature in said first image using iterative gray-level thresholding; detecting a second feature contour of said anatomic feature in said second image; and superposing said first feature contour and said second feature contour; wherein performing iterative gray-level thresholding comprises; obtaining a global gray-level histogram of said first image including said anatomic feature; obtaining a plurality of global gray-level threshold values using said global gray-level histogram; generating a binary image using each of said plurality of global gray-level threshold values; and determining a first initial contour using said binary images; and wherein determining said first initial contour comprises; selecting pixels in said first image having a gray-level value greater than a predetermined gray-level threshold value and less than a corresponding one of said global gray-level threshold values to obtain at least one region; producing a boundary of said at least one region; determining whether said at least one region is located within said anatomic feature; eliminating a region determined not to be in said anatomic feature; and repeating said steps of selecting pixels, producing a boundary, determining whether said at least one region is located within said anatomic feature and eliminating a region for each of remaining ones of said global gray-level threshold values. - View Dependent Claims (16, 17, 18)
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19. A method as of correlating images from two modalities, comprising:
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obtaining a first image containing an anatomic feature using a first modality; obtaining a second image containing said anatomic feature using a second modality; detecting a first feature contour of said anatomic feature in said first image using iterative gray-level thresholding; detecting a second feature contour of said anatomic feature in said second image; and superposing said first feature contour and said second feature contour; wherein performing iterative gray-level thresholding comprises; performing global gray-level thresholding to obtain a first initial contour of said anatomic feature; and performing local gray-level thresholding using said first initial contour to obtain said first feature contour; and wherein performing local gray-level thresholding comprises; placing a plurality of regions of interest (ROIs) on said first initial contour; producing a respective plurality of gray-level histograms of pixels located within each of said plurality of ROIs; determining a respective plurality of gray-level threshold values using said plurality of gray-level histograms; selecting pixels within said ROIs using respective ones of said gray-level thresholds; generating a binary image using said selected pixels; and detecting a boundary of said binary image as said first feature contour. - View Dependent Claims (20, 21, 22)
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23. A method of correlating images from two modalities, comprising:
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obtaining a radiographic chest image containing lung having a plurality of pixels as a first image using a first modality; obtaining a second image containing said lung using a second modality; detecting an initial lung contour in said first image using iterative gray-level thresholding; placing a plurality of regions of interest (ROIs) on said initial lung contour; selecting a gray-level threshold value in each of said ROIs; selecting pixels in said ROIs based upon corresponding ones of said gray-level threshold values; detecting a second lung contour in said second image; and superposing said first and second images. - View Dependent Claims (24, 25, 26)
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27. A method of correlating images from two modalities, comprising:
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obtaining a first image containing an anatomic feature using a first modality; obtaining a second image containing said anatomic feature using a second modality; detecting a first feature contour of said anatomic feature in said first image using iterative gray-level thresholding; detecting a second feature contour of said anatomic feature in said second image; and
superposing said first feature contour and said second feature contour;wherein said superposing step comprises; determining a first ratio of a first maximum width of said first feature contour to a second maximum width of said second feature contour; determining a second ratio of a first maximum height of said first feature contour to a second maximum height of said second feature contour; using said first and second ratios as first and second direction scaling factors, respectively; and scaling said first feature contour using said first and second ratios. - View Dependent Claims (28, 29)
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30. A method as of correlating images from two modalities, comprising:
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obtaining a first image containing an anatomic feature using a first modality; obtaining a second image containing said anatomic feature using a second modality; detecting a first feature contour of said anatomic feature in said first image using iterative gray-level thresholding; detecting a second feature contour of said anatomic feature in said second image; superposing said first feature contour and said second feature contour; obtaining a chest image as said first and second images; obtaining first and second lung contours as said first and second feature contours; determining a first ratio of a first maximum width of said first lung contour to a second maximum width of said second lung contour; determining a second ratio of a first maximum height of said first lung contour to a second maximum height of said second lung contour; using said first and second ratios as first and second direction scaling factors, respectively; and scaling said first feature contour using said first and second ratios. - View Dependent Claims (31)
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32. A method of correlating images from two modalities, comprising:
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obtaining a first image containing an anatomic feature using a first modality; obtaining a second image containing said anatomic feature using a second modality; detecting a first feature contour of said anatomic feature in said first image using iterative gray-level thresholding; detecting a second feature contour of said anatomic feature in said second image; superposing said first feature contour and said second feature contour; obtaining a radiographic image as said first image; and performing iterative gray-level thresholding on said radiographic image; wherein performing iterative gray-level thresholding comprises; placing a plurality of ROIs on said radiographic image; generating a plurality of gray-level histograms for each of said ROIs, respectively; determining a plurality of gray-level threshold values for each of said ROIs, respectively, using corresponding ones of said gray-level histograms; and modifying a gray-level value of a predetermined pixel in selected ones of said ROIs using respective ones of said gray-level threshold values.
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33. A system for correlating images from two different modalities, comprising:
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a first image acquisition device; a second image acquisition device; a first contouring circuit to provide first contours of a feature in an image obtained using said first image acquisition device by iteratively using estimated contours derived from a plurality of successive gray-level thresholding operations at a respective plurality of gray-level thresholds, including thresholding said first image using one of said plurality of gray-level thresholds, determining whether to alter pixels in said first image, and thresholding said first image using a subsequent one of said gray-level thresholds after said determining step; a second contouring circuit, connected to said second image acquisition device, to provide second contours of said feature in said second image; a superposition circuit connected to said first and second contouring circuits; and a display. - View Dependent Claims (34, 35, 36, 37)
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38. A method of determining an image contour, comprising:
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obtaining a first image containing an anatomic feature; obtaining a plurality of gray-level thresholds in said first image; thresholding said first image using one of said plurality of gray-level thresholds; determining whether to alter pixels in said first image based upon said thresholding; thresholding said first image using a second one of said plurality of gray-level thresholds after said determining step; generating a plurality of initial contours using said thresholding steps; and detecting a first feature contour of said anatomic feature using said plurality of initial contours. - View Dependent Claims (39, 40, 41, 42, 43, 44, 45, 46, 47, 48)
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49. A method of determining a feature contour, comprising:
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obtaining a digitized chest image containing at least one lung; obtaining a global gray-level histogram of a selected portion of said digitized chest image; identifying lung and mediastinum peaks in said global gray-level histogram; identifying a minimum in said global gray-level histogram between said lung and mediastinum peaks; selecting a plurality of gray-level threshold values between said minimum and said lung peak; and detecting a feature contour of said at least one lung in said chest image by iteratively using said plurality of gray-level threshold values. - View Dependent Claims (50, 51, 52)
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Specification