Method and system for automated selection of regions of interest and detection of septal lines in digital chest radiographs
First Claim
1. A computer implemented method for automated detection of abnormalities in digital chest radiographs comprising:
- identifying peripheral lung areas of a digital chest radiograph, stored in an image memory in a computer, to be analyzed;
preselecting, using said computer, a sample of numerous contiguous regions of interest (ROIs) included in said peripheral lung areas;
performing a background trend correction, using said computer, on each of the ROIs of said preselected sample so as to produce corrected image data;
performing an edge gradient analysis, using said computer, on each of the ROIs of said preselected sample in order to distinguish sharp-edged ROIs from ROIs with sharp edges;
removing a portion of said sharp-edged ROIs from said preselected sample; and
performing texture measurements on the remaining ROIs of said preselected sample which have not been removed in said removing step.
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Accused Products
Abstract
An automated method and system for discriminating between normal lungs and abnormal lungs having interstitial disease and/or septal lines, wherein a large number of adjacent regions of interest (ROIs) are selected, corresponding to an area on a digital image of a patient'"'"'s lungs. The ROIs each contain a number of square or rectangular pixel arrays and are selected to sequentially fill in the total selected area of the lungs to be analyzed. A background trend is removed from each individual ROI and the ROIs are then analyzed to determine those exhibiting sharp edges, i.e., high edge gradients. A percentage of these sharp-edged ROIs are removed from the original sample based on the edge gradient analysis, a majority of which correspond to rib-edge containing ROIs. After removal of the sharp-edged ROIs, texture measurements are taken on the remaining sample in order to compare such data with predetermined data for normal and abnormal lungs. Thus, a computerized scheme for quantitative analysis of interstitial lung diseases and/or septal lines appearing in digitized chest radiographs can be implemented in practical clinical situations.
209 Citations
42 Claims
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1. A computer implemented method for automated detection of abnormalities in digital chest radiographs comprising:
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identifying peripheral lung areas of a digital chest radiograph, stored in an image memory in a computer, to be analyzed; preselecting, using said computer, a sample of numerous contiguous regions of interest (ROIs) included in said peripheral lung areas; performing a background trend correction, using said computer, on each of the ROIs of said preselected sample so as to produce corrected image data; performing an edge gradient analysis, using said computer, on each of the ROIs of said preselected sample in order to distinguish sharp-edged ROIs from ROIs with sharp edges; removing a portion of said sharp-edged ROIs from said preselected sample; and performing texture measurements on the remaining ROIs of said preselected sample which have not been removed in said removing step. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11)
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12. A computer system for automated detection of abnormalities in digital chest radiographs, comprising:
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means for identifying peripheral lung areas of a digital chest radiograph to be analyzed; means for preselecting a sample of numerous contiguous regions of interest (ROIs) included in said peripheral lung areas; means for performing a background trend correction on each of the ROIs of said preselected sample so as to produce corrected image data; means for performing an edge gradient analysis on each of the ROIs of said preselected sample in order to distinguish sharp-edged ROIs from ROIs without sharp edges; means for removing a portion of said sharp-edged ROIs from said preselected sample; and means for performing texture measurements on the remaining ROIs of said preselected sample which have not been removed in said removing step. - View Dependent Claims (13, 14, 15, 16, 17, 18, 19, 20, 21, 22)
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23. A computer implemented method for automated detection of septal lines in a digital chest radiograph, stored in an image memory in a computer, comprising:
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identifying a lower lung area along rib cage edges in said digital chest radiograph; preselecting, using said computer, a sample of numerous contiguous regions of interest (ROIs) within said lower lung area; performing contrast enhancement, in said computer, using an unsharp masking technique with a rectangular mask; removing a background trend from each individual ROI; calculating accumulated edge gradients for each of said individual ROIs; generating an edge-gradient orientation histogram, in said computer, for each of said individual ROIs based on said calculated accumulated edge gradients; comparing, using said computer, each generated edge-gradient orientation histogram with predetermined values stored in a database to obtain a comparison result for each of said individual ROIs; determining if septal lines are present in each of said individual ROIs based on said comparison result for each of said individual ROIs; and classifying, using said computer, each of said individual ROIs as normal or abnormal having septal lines. - View Dependent Claims (24, 25, 26, 27, 28, 29, 30, 31, 32)
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33. A computer system for automated detection of septal lines in digital chest radiographs, comprising:
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means for identifying a lower lung area along rib cage edges; means for preselecting a sample of numerous contiguous regions of interest (ROIs) within said lower lung area; means for performing contrast enhancement using an unsharp masking technique with a rectangular mask; means for removing a background trend from each individual ROI; means for calculating accumulated edge gradients for each of said individual ROIs; means for generating an edge-gradient orientation histogram for each individual ROI based on said calculated accumulated edge gradients; means for comparing each generated edge-gradient orientation histogram with predetermined values stored in a database to obtain a comparison result for each individual ROI; means for determining if septal lines are present in each of said individual ROIs based on said comparison result for each of said individual ROIs; and means for classifying each of said individual ROIs as normal or as abnormal having septal lines. - View Dependent Claims (34, 35, 36, 37, 38, 39, 40, 41, 42)
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Specification