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:
Dec. 27, 2011
Filed:
Oct. 03, 2008
Michelle Xiao-hong Yan, Princeton, NJ (US);
Ti-chiun Chang, Princeton Junction, NJ (US);
Markus Lendl, Ottensoos, DE;
Stefan Boehm, Oberasbach, DE;
Tong Fang, Morganville, NJ (US);
Peter Durlak, Erlangen, DE;
Michelle Xiao-Hong Yan, Princeton, NJ (US);
Ti-chiun Chang, Princeton Junction, NJ (US);
Markus Lendl, Ottensoos, DE;
Stefan Boehm, Oberasbach, DE;
Tong Fang, Morganville, NJ (US);
Peter Durlak, Erlangen, DE;
Siemens Aktiengesellschaft, Munich, DE;
Abstract
A method and system for image quality assessment is disclosed. The image quality assessment method is a no-reference method for objectively assessing the quality of medical images. This method is guided by the human vision model in order to accurately reflect human perception. A region of interest (ROI) of medical image is divided into non-overlapping blocks of equal size. Each of the blocks is categorized as a smooth block, a texture block, or an edge block. A perceptual sharpness measure, which is weighted by local contrast, is calculated for each of the edge blocks. A perceptual noise level measure, which is weighted by background luminance, is calculated for each of the smooth blocks. A sharpness quality index is determined based on the perceptual sharpness measures of all of the edge blocks, and a noise level quality index is determined based on the perceptual noise level measures of all of the smooth blocks. An overall image quality index can be determined by using task specific machine learning of samples of annotated images. The image quality assessment method can be used in applications, such as video/image compression and storage in healthcare and homeland security, and band-width limited wireless communication.