Automated radiograph classification using anatomy information
AssigneeCarestream Health, Inc.
InventorsHui Luo, Xiaohui Wang
Life of this patent
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Abstract
A method for automatically classifying a radiograph. A digital radiographic image is acquired, wherein the image is comprised of a matrix of rows and columns of pixels. The digital image is segmented into foreground, background, and anatomy regions. A physical size of the anatomy region is classified. An edge direction histogram of the anatomy region is generated and a shape pattern of the edge direction histogram is classified. Based on the physical size classification and the shape pattern classification, the image is categorized.
Claims5
A method for automatically classifying a digital radiographic image, comprising the following steps, carried out by a programmable digital computer, of:
- acquiring the digital radiographic image comprised of a matrix of rows and columns of pixels;
- segmenting the image into foreground, background, and anatomy regions;
- classifying a physical size of the anatomy region using image capture information;
- classifying a shape pattern of the anatomy region;
- and
- categorizing the image based on the physical size classification and the shape pattern classification;
- wherein the step of classifying a physical size comprises:
- collecting image capture parameters
- computing features from the foreground, background and anatomy regions;
- training a classifier with the features and image capture parameters;
- and
- performing the classification using the trained classifier;
- and
- wherein said features and said image capture parameters include one or more of the following:
- a ratio of the anatomy's area to the image area, a ratio of the background area to the image area, and a ratio of the foreground area to the image area.
A method for automatically classifying a digital radiographic image, comprising the following steps, carried out by a programmable digital computer, of:
- acquiring the digital radiographic image comprised of a matrix of rows and columns of pixels;
- segmenting the image into foreground, background, and anatomy regions;
- classifying a physical size of the anatomy region using image capture information;
- classifying a shape pattern of the anatomy region;
- and
- categorizing the image based on the physical size classification and the shape pattern classification:
- wherein the step of classifying the shape pattern comprises the steps of;
- extracting edges of the anatomy regions;
- generating an edge direction histogram of the anatomy region;
- and
- classifying the edge direction histogram using a scale and rotation invariant shape classifier;
- and
- wherein the step of categorizing the image categorizes the images according to at least one of the following classes:
- a large-size anatomy with no edge, such as the PA (Posterior-Anterior) view of abdomen, thorax spine and lumbar spine;
- a large-size anatomy with a one-peak shape edge direction histogram, such as the PA view of hip and shoulder;
- a large-size anatomy with a two-peak shape edge direction histogram, such as the lateral (LAT) view of chest and the PA view of pelvis;
- a large-size anatomy with an other share edge direction histogram, such as the PA and LAT view of skull;
- a small-size anatomy with no edge, such some PA and LAT view of knee because of the setting of collimator;
- a small-size anatomy with a one-peak shape edge direction histogram, such as some PA and LAT view of femur, elbow, forearm and ankle, in which the collimation region covers parts anatomy and results in only one edge detected in the image;
- a small-size anatomy with a two-peak shape edge direction histogram, such as most PA and LAT view of elbow, forearm, ankle and wrist;
- a small-size anatomy with an other share edge direction histogram, such as the PA and LAT view of foot, hand and fingers.
Dependencies are inferred from the claim text.