Qualia Computing, Inc.

Edward M Ochoa

Steven Keith Rogers

John E Rosenstengel

Randy P Broussard

Telford S Berkey

Martin P DeSimio

Philip Amburn

Jeffrey W Hoffmeister

Thomas F Rathbun

Thomas P Rathbun

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.

Method for combining automated detections from medical images with observed detections of a human interpreter

Joint optimization of parameters for the detection of clustered microcalcifications in digital mammograms

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.

Method and system for automated detection of clustered microcalcifications from digital mammograms

Method and system for segmentation and detection of microcalcifications

Method and system for combining automated detections from digital

Method and system for segmenting desired regions in digital mammograms

Method and system for automated detection of clustered

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San Antonio, TX, United States of America

Edward M Ochoa