The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.

The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.

Date of Patent:
Jun. 26, 2012

Filed:

Dec. 13, 2005
Applicants:

James David Schaffer, Wappingers Falls, NY (US);

Walid Ali, Croton-On-Hudson, NY (US);

Larry J. Eshelman, Ossining, NY (US);

Claude Cohen-bacrie, New York, NY (US);

Jean-michel Lagrange, Moissy Cramayel, FR;

Claire Levrier, Rueil-Malmaison, FR;

Nicholas Villain, Clamart, FR;

Robert R. Entrekin, Kirkland, WA (US);

Inventors:

James David Schaffer, Wappingers Falls, NY (US);

Walid Ali, Croton-On-Hudson, NY (US);

Larry J. Eshelman, Ossining, NY (US);

Claude Cohen-Bacrie, New York, NY (US);

Jean-Michel Lagrange, Moissy Cramayel, FR;

Claire Levrier, Rueil-Malmaison, FR;

Nicholas Villain, Clamart, FR;

Robert R. Entrekin, Kirkland, WA (US);

Assignee:
Attorney:
Primary Examiner:
Assistant Examiner:
Int. Cl.
CPC ...
G06K 9/36 (2006.01);
U.S. Cl.
CPC ...
Abstract

A method for determining the presence or absence of malignant features in medical images, wherein a plurality of base comparison or training images of various types of lesions taken of actual patient is examined by one or more image reading experts to create a first database array. Low-level features of each of the lesions in the same plurality of base comparisons or training images are determined using one or more image processing algorithms to obtain a second database array set. The first and second database array set are combined to create a training database array set which is input to a learning system that discovers/learns a classifier that maps from a subset of the low-level features to the expert's evaluation in the first database array set. The classifier is used to determine the presence of a particular mid-level feature in an image of lesion in a patient based solely on the image.


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