Technique for finding the histogram region of interest for improved tone scale reproduction of digital radiographic images
First Claim
1. A method of finding a histogram region of interest for improved tone scale reproduction of digital radiographic images comprising the steps of:
- pseudo-randomly sampling a digital radiographic image with a sample having an appropriate size to delineate a region of interest;
processing each sample using texture analysis techniques to extract a plurality of texture features;
using the extracted texture features, classifying said sample with a previously trained neural network classifier to determine its class; and
accumulating pixel values belonging to the same class to form separate histograms for each said class, wherein each said histogram can be used to optimize tone scale reproduction.
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Abstract
An image processing technique especially useful in processing digital radiographic images. A method for finding a histogram region of interest for improved tone scale reproduction of digital radiographic images includes the following steps. A digital radiographic image is randomly sampled with a sample having an appropriate size to delineate an object of interest. Each sample is processed using texture analysis techniques to extract a plurality of texture features. Using the extracted texture features, each sample is classified with a previously trained neural network classifier to determine its class. Last, the pixel values belonging to the same class are accumulated to form separate histograms for each class. Each of the histograms are then used to optimize tone scale reproduction.
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Citations
9 Claims
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1. A method of finding a histogram region of interest for improved tone scale reproduction of digital radiographic images comprising the steps of:
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pseudo-randomly sampling a digital radiographic image with a sample having an appropriate size to delineate a region of interest; processing each sample using texture analysis techniques to extract a plurality of texture features; using the extracted texture features, classifying said sample with a previously trained neural network classifier to determine its class; and accumulating pixel values belonging to the same class to form separate histograms for each said class, wherein each said histogram can be used to optimize tone scale reproduction. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9)
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Specification