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. 23, 2018

Filed:

Jan. 11, 2016
Applicant:

Carl Zeiss X-ray Microscopy Inc., Pleasanton, CA (US);

Inventors:

Sreenivas Naga Bhattiprolu, Dublin, CA (US);

Tom Waite, El Sobrante, CA (US);

Assignee:

CARL ZEISS X-RAY MICROSCOPY INC., Pleasanton, CA (US);

Attorney:
Primary Examiner:
Assistant Examiner:
Int. Cl.
CPC ...
G01N 23/046 (2018.01); G01N 23/22 (2018.01); G06T 7/40 (2017.01); G06K 9/66 (2006.01); G06T 7/00 (2017.01); G06T 7/20 (2017.01); G06T 5/10 (2006.01); G01V 5/00 (2006.01); G06K 9/00 (2006.01); G06K 9/46 (2006.01);
U.S. Cl.
CPC ...
G01N 23/22 (2013.01); G01N 23/046 (2013.01); G01V 5/00 (2013.01); G06K 9/00664 (2013.01); G06K 9/4619 (2013.01); G06K 9/66 (2013.01); G06T 5/10 (2013.01); G06T 7/0028 (2013.01); G06T 7/2033 (2013.01); G06T 7/40 (2013.01); C01P 2002/85 (2013.01); G01N 2223/402 (2013.01); G01N 2223/405 (2013.01); G01N 2223/616 (2013.01); G06K 2209/19 (2013.01); G06T 2207/10061 (2013.01); G06T 2207/10081 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/20084 (2013.01); G06T 2207/30181 (2013.01);
Abstract

A multimodality imaging system and method for mineralogy segmentation is disclosed. Image datasets of the sample are generated for one or more modalities, including x-ray and focused ion beam scanning electron microscope (FIB-SEM) modalities. Mineral maps are then created using Energy Dispersive X-ray spectroscopy (EDX) from at least part of the sample covered by the image datasets. The EDX mineral maps are applied as a mask to the image datasets to identify and label regions of minerals within the sample. Feature vectors are then extracted from the labeled regions via feature generators such as Gabor filters. Finally, machine learning training and classification algorithms such as Random Forest are applied to the extracted feature vectors to construct a segmented image representation of the sample that classifies the minerals within the sample.


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