Icad, Inc.

Ryan McGinnis

Michael J Collins

Kevin S Woods

Brent Woods

William Pierson

Senthil Periaswamy

Robert L Van Uitert

Systems and methods are presented that detect and classify mass-like regions exhibiting spiculated and/or dense characteristics with high sensitivity and at acceptable false positive rates. One or more suspicious masses are identified in medical imagery of the breast. In certain embodiments, a quantitative measure of spiculation and quantitative measure of density are computed for each suspicious mass located. At least one classification scheme, developed using true and false positives with similar quantitative measures, is then selected for each suspicious mass according to both quantitative measures. In certain other embodiments, a measure of breast location is computed for each suspicious mass. In one embodiment, the location determines whether a suspicious mass appears inside or outside of the parenchyma region of the breast.

Computer-aided detection and classification of suspicious masses in breast imagery

Systems, computer-readable media, and methods are presented that identify suspicious anomalies in a colon with higher sensitivity and at a lower false positive rate. A plurality of images of an anatomical colon is acquired. Candidate suspicious anomalies are identified in each image. The candidate suspicious anomalies across images are then compared using registration and matching. Features of candidate suspicious anomalies across images may be jointly evaluated to perform classification.

Systems and methods for identifying suspicious anomalies using information from a plurality of images of an anatomical colon under study

Methods are presented that detect and classify mass-like regions exhibiting spiculated and/or dense characteristics with high sensitivity and at acceptable false positive rates. One or more suspicious masses are identified in medical imagery of the breast. In certain embodiments, a quantitative measure of spiculation and quantitative measure of density are computed for each suspicious mass located. At least one classification scheme, developed using true and false positives with similar quantitative measures, is then selected for each suspicious mass according to both quantitative measures. In certain other embodiments, a measure of breast location is computed for each suspicious mass. In one embodiment, the location determines whether a suspicious mass appears inside or outside of the parenchyma region of the breast.

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London, OH, United States of America

Ryan McGinnis