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:
Oct. 22, 2019

Filed:

Feb. 23, 2018
Applicant:

University of Louisville Research Foundation, Inc., Louisville, KY (US);

Inventors:

Ayman S. El-Baz, Louisville, KY (US);

Amy Dwyer, Crestwood, KY (US);

Rosemary Ouseph, Louisville, KY (US);

Fahmi Khalifa, Louisville, KY (US);

Ahmed Soliman, Louisville, KY (US);

Mohamed Shehata, Louisville, KY (US);

Attorneys:
Primary Examiner:
Assistant Examiner:
Int. Cl.
CPC ...
G16H 30/40 (2018.01); A61B 5/20 (2006.01); G06F 19/00 (2018.01); G06T 7/00 (2017.01); G06T 7/33 (2017.01); G06T 7/12 (2017.01); G06T 7/143 (2017.01); G16H 50/20 (2018.01); G16H 30/20 (2018.01);
U.S. Cl.
CPC ...
G16H 30/40 (2018.01); A61B 5/201 (2013.01); G06F 19/321 (2013.01); G06T 7/0012 (2013.01); G06T 7/12 (2017.01); G06T 7/143 (2017.01); G06T 7/33 (2017.01); G16H 30/20 (2018.01); G16H 50/20 (2018.01); G06T 2207/10088 (2013.01); G06T 2207/10096 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/30084 (2013.01);
Abstract

A computer aided diagnostic system and automated method to classify a kidney. Image data for a medical scan that includes image data of a kidney may be received. The kidney image data may be segmented from other image data of the medical scan. One or more iso-contours may be registered for the kidney image data, and renal cortex image data may be segmented from the kidney image data based on the one or more registered iso-contours. The kidney may be classified by analyzing one or more features determined from the segmented renal cortex image data using a learned model associated with the one or more features.


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