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.:
Date of Patent:
Sep. 21, 2010
Filed:
Jul. 18, 2005
Zhengrong Ying, Wakefield, MA (US);
Ram Naidu, Newton, MA (US);
Sergey Simanovsky, Brookline, MA (US);
Matthew Hirsch, Cambridge, MA (US);
Carl R. Crawford, Brookline, MA (US);
Zhengrong Ying, Wakefield, MA (US);
Ram Naidu, Newton, MA (US);
Sergey Simanovsky, Brookline, MA (US);
Matthew Hirsch, Cambridge, MA (US);
Carl R. Crawford, Brookline, MA (US);
Analogic Corporation, Peabody, MA (US);
Abstract
A method of and a system for identifying objects using local distribution features from multi-energy CT images are provided. The multi-energy CT images include a CT image, which approximates density measurements of scanned objects, and a Z image, which approximates effective atomic number measurements of scanned objects. The local distribution features are first and second order statistics of the local distributions of the density and atomic number measurements of different portions of a segmented object. The local distributions are the magnitude images of the first order derivative of the CT image and the Z image. Each segmented object is also divided into different portions to provide geometrical information for discrimination. The method comprises preprocessing the CT and Z images, segmenting images into objects, computing local distributions of the CT and Z images, computing local distribution histograms, computing local distribution features from the said local distribution histograms, classifying objects based on the local distribution features.