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:
Dec. 09, 2014

Filed:

Dec. 21, 2011
Applicants:

Yong Fan, Beijing, CN;

Hongming LI, Beijing, CN;

Inventors:

Yong Fan, Beijing, CN;

Hongming Li, Beijing, CN;

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06K 9/00 (2006.01); G06T 7/00 (2006.01);
U.S. Cl.
CPC ...
G06T 7/0081 (2013.01); G06T 2207/30016 (2013.01); G06T 2207/30096 (2013.01); G06T 2207/20081 (2013.01); G06T 7/0087 (2013.01); G06T 2207/20076 (2013.01); G06T 2207/10088 (2013.01); Y10S 128/922 (2013.01);
Abstract

A method for brain tumor segmentation in multi-parametric 3D magnetic resonance (MR) images, comprising: determining, for each voxel in the multi-parametric 3D MR image sequence, a probability that the voxel is part of brain tumor; extracting multi-scale structure information of the image; generating multi-scale tumor probability map based on initial tumor probability at voxel level and multi-scale structure information; determining salient tumor region based on multi-scale tumor probability map; obtaining robust initial tumor and non-tumor label based on tumor probability map at voxel level and salient tumor region; and generating a segmented brain tumor image using graph based label information propagation. The present invention is capable of achieving statistical reliable, spatially compact, and robust tumor label initialization, which is helpful to the accurate and reliable tumor segmentation. And the label information propagation framework could partially alleviate the performance degradation caused by image inconsistency between images to be segmented and training images.


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