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:
Mar. 28, 2017

Filed:

Dec. 05, 2012
Applicants:

The Johns Hopkins University, Baltimore, MD (US);

The United States of America, As Represented BY the Secretary Department of Health and Human Services, Washington, DC (US);

Inventors:

Ciprian M. Crainiceanu, Baltimore, MD (US);

Elizabeth M. Sweeney, Baltimore, MD (US);

Russell T. Shinohara, Baltimore, MD (US);

Arthur J. Goldsmith, Baltimore, MD (US);

Daniel Reich, Washington, DC (US);

Colin Shea, Great Falls, VA (US);

Assignee:

The Johns Hopkins University, Baltimore, MD (US);

Attorneys:
Primary Examiner:
Assistant Examiner:
Int. Cl.
CPC ...
G06K 9/00 (2006.01); G06T 7/00 (2017.01); A61B 5/055 (2006.01); A61B 5/00 (2006.01); A61B 6/03 (2006.01); A61B 8/00 (2006.01); G01R 33/48 (2006.01); G01R 33/50 (2006.01); G01T 1/16 (2006.01); G06F 19/00 (2011.01); G01R 33/56 (2006.01);
U.S. Cl.
CPC ...
G06T 7/0081 (2013.01); A61B 5/055 (2013.01); A61B 5/7264 (2013.01); A61B 6/032 (2013.01); A61B 6/037 (2013.01); A61B 8/00 (2013.01); G01R 33/4806 (2013.01); G01R 33/50 (2013.01); G01T 1/16 (2013.01); G06F 19/345 (2013.01); G06T 7/0012 (2013.01); G06T 7/0016 (2013.01); G06T 7/0034 (2013.01); G06T 7/0087 (2013.01); G01R 33/5602 (2013.01); G01R 33/5608 (2013.01); G06T 2207/10004 (2013.01); G06T 2207/10081 (2013.01); G06T 2207/10084 (2013.01); G06T 2207/10088 (2013.01); G06T 2207/10116 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/20141 (2013.01); G06T 2207/20224 (2013.01); G06T 2207/30016 (2013.01); G06T 2207/30096 (2013.01);
Abstract

A method of automatically detecting tissue abnormalities in images of a region of interest of a subject includes obtaining first image data for the region of interest of the subject, normalizing the first image data based on statistical parameters derived from at least a portion of the first image data to provide first normalized image data, obtaining second image data for the region of interest of the subject, normalizing the second image data based on statistical parameters derived from at least a portion of the second image data to provide second normalized image data, processing the first and second normalized image data to provide resultant image data, and generating a probability map for the region of interest based on the resultant image data and a predefined statistical model. The probability map indicates the probability of at least a portion of an abnormality being present at locations within the region of interest.


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