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:
Sep. 03, 2019

Filed:

May. 12, 2017
Applicant:

International Business Machines Corporation, Armonk, NY (US);

Inventors:

Mani Abedini, Melbourne, AU;

Rajib Chakravorty, Epping, AU;

Rahil Garnavi, Macleod, AU;

Munawar Hayat, Coburg, AU;

Attorneys:
Primary Examiner:
Int. Cl.
CPC ...
G06T 7/10 (2017.01); G06T 7/13 (2017.01); G06K 9/46 (2006.01); G06K 9/52 (2006.01); G06K 9/62 (2006.01); G06T 7/00 (2017.01); G06K 9/68 (2006.01); G06T 7/143 (2017.01); G06T 7/90 (2017.01); G06T 7/11 (2017.01);
U.S. Cl.
CPC ...
G06T 7/13 (2017.01); G06K 9/4604 (2013.01); G06K 9/52 (2013.01); G06K 9/6256 (2013.01); G06K 9/6262 (2013.01); G06K 9/6267 (2013.01); G06K 9/6857 (2013.01); G06T 7/0012 (2013.01); G06T 7/10 (2017.01); G06T 7/11 (2017.01); G06T 7/143 (2017.01); G06T 7/90 (2017.01); G06T 2207/20016 (2013.01); G06T 2207/20021 (2013.01); G06T 2207/20076 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/20112 (2013.01); G06T 2207/30088 (2013.01); G06T 2207/30096 (2013.01);
Abstract

A robust segmentation technique based on multi-layer classification technique to identify the lesion boundary is described. The inventors have discovered a technique based on training several classifiers such that to classify each pixel as lesion versus normal Each classifier is trained on a specific range of image resolutions. Then, for a new test image, the trained classifiers are applied on the image. Then by fusing the prediction results in pixel level a probability map is generated. In the next step, a thresholding method is applied to convert the probability map to a binary mask, which determines a mole border.


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