Automated method of patient recognition using chest radiographs
First Claim
1. A method for determining whether a first medical image and a second medical image are medical images of a same patient, comprising:
- selecting a first region in the first medical image;
selecting a second region in the second medical image;
determining a region common to the first region and the second region based on a boundary of the first region and a boundary of the second region;
calculating a correlation coefficient based on image data from the first medical image in the common region and image data from the second medical image in the common region; and
determining whether the first medical image and the second medical image are medical images of the same patient based on the correlation coefficient.
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Abstract
A method for determining whether a first medical image and a second medical image are medical images of the same patient, comprising selecting a first region in the first medical image; selecting a second region in the second medical image; determining a common region based on a boundary of the first region and a boundary of the second region; calculating a correlation coefficient based on image data from the first medical image in the common region and image data from the second medical image in the common region; and determining whether the first medical image and the second medical image are medical images of the same patient based on the correlation coefficient. Biological fingerprints from parts of chest radiographs such as thoracic fields, cardiac shadows, lung apices, superior mediastinum, and the right lower lung that includes the costophrenic angle, are used for the purpose of patient recognition and identification.
22 Citations
8 Claims
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1. A method for determining whether a first medical image and a second medical image are medical images of a same patient, comprising:
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selecting a first region in the first medical image;
selecting a second region in the second medical image;
determining a region common to the first region and the second region based on a boundary of the first region and a boundary of the second region;
calculating a correlation coefficient based on image data from the first medical image in the common region and image data from the second medical image in the common region; and
determining whether the first medical image and the second medical image are medical images of the same patient based on the correlation coefficient. - View Dependent Claims (2, 3, 4, 5, 7, 8)
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6. A method for determining whether a first medical image and a second medical image are medical images of a same patient, comprising:
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selecting a plurality of first regions, each first region corresponding to one of a thoracic field, a cardiac shadow, lung apex, a superior mediastinum, and a right lower lung in the first medical image;
selecting a respective plurality of second regions in the second medical image based on the plurality of first regions;
determining respective regions common to the plurality of first regions and the respective plurality of second regions;
calculating a set of correlation coefficients based on image data from the first medical image in each respective common region and image data from the second medical image in each respective common region; and
determining whether the first medical image and the second medical image are medical images of the same patient using an artificial neural network having the set of correlation coefficients as inputs.
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Specification