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AssigneeQualia Computing, Inc.
InventorsSteven K. Rogers, Philip Amburn, Telford S. Berkey, Randy P. Broussard, Martin P. DeSimio, Jeffrey W. Hoffmeister, Edward M. Ochoa, Thomas F. Rathbun, John E. Rosenstengel
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A method and system for detecting and displaying clustered microcalcifications in a digital mammogram, wherein a single digital mammogram is first automatically cropped to a breast area sub-image which is then processed by means of an optimized Difference of Gaussians filter to enhance the appearance of potential microcalcifications in the sub-image. The potential microcalcifications are thresholded, clusters are detected, features are computed for the detected clusters, and the clusters are classified as either suspicious or not suspicious by means of a neural network. Thresholding is preferably by sloping local thresholding, but may also be performed by global and dual-local thresholding. The locations in the original digital mammogram of the suspicious detected clustered microcalcifications are indicated. Parameters for use in the detection and thresholding portions of the system are computer-optimized by means of a genetic algorithm. The results of the system are optimally combined with a radiologist's observation of the original mammogram by combining the observations with the results, after the radiologist has first accepted or rejected individual detections reported by the system.
A method for automatically segmenting a desired region in a digital mammogram image from undesired regions, comprising the steps of:
A method for segmenting an area of a digital mammogram image corresponding to breast tissue from the remainder of the image, comprising the steps of:
A system for automatically segmenting a desired region in a digital mammogram image from undesired regions, comprising:
A system for segmenting an area of a digital mammogram image corresponding to breast tissue from the remainder of the image, comprising:
Dependencies are inferred from the claim text.