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.

Patent No.:

US 9269156 B1

PDF
Full Text
Expired
Date of Patent:
Feb. 23, 2016

Filed:

Jul. 22, 2013
Applicants:

Neil Birkbeck, Plainsboro, NJ (US);

Jingdan Zhang, Plainsboro, NJ (US);

Martin Requardt, Nuremberg, DE;

Berthold Kiefer, Erlangen, DE;

Peter Gall, Buckenhof, DE;

Shaohua Kevin Zhou, Plainsboro, NJ (US);

Inventors:

Neil Birkbeck, Plainsboro, NJ (US);

Jingdan Zhang, Plainsboro, NJ (US);

Martin Requardt, Nuremberg, DE;

Berthold Kiefer, Erlangen, DE;

Peter Gall, Buckenhof, DE;

Shaohua Kevin Zhou, Plainsboro, NJ (US);

Assignee:
Attorney:
Primary Examiner:
Assistant Examiner:
Int. Cl.
CPC ...
G06T 7/00 (2006.01); G06T 5/00 (2006.01);
U.S. Cl.
CPC ...
G06T 7/0081 (2013.01); G06T 5/009 (2013.01); G06T 7/0089 (2013.01); G06T 2200/04 (2013.01); G06T 2207/10088 (2013.01); G06T 2207/20016 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/30081 (2013.01);
Abstract

A method and system for fully automatic segmentation the prostate in magnetic resonance (MR) image data is disclosed. Intensity normalization is performed on an MR image of a patient to adjust for global contrast changes between the MR image and other MR scans and to adjust for intensity variation within the MR image due to an endorectal coil used to acquire the MR image. An initial prostate segmentation in the MR image is obtained by aligning a learned statistical shape model of the prostate to the MR image using marginal space learning (MSL). The initial prostate segmentation is refined using one or more trained boundary classifiers.


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