By Topic

A Non Local Means Method Using Fuzzy Similarity Criteria for Restoration of Ultrasound Images

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Binaee, K. ; Dept. of Electr. Eng., Univ. of Guilan, Rasht, Iran ; Hasanzadeh, R.P.R.

Conventional Non-Local Means (NLM) as one of the most powerful denoising filters especially for reduction of additive Gaussian noise is not successful in the case of Ultrasound (US) Images noise suppression. In the presence of additive Gaussian noise model, the NLM filter uses Euclidean distance similarity criterion to find similar patches and therefore it is not appropriate for US images which have noise with multiplicative and signal dependant nature. The more successful version of NLM filter for US images which is known as Optimized Bayesian NLM (OBNLM) is developed based on Pearson Distance similarity criterion to measure and find the similar patches. In this paper, we tried to improve the performance of NLM filter using appropriate fuzzy similarity criteria. The proposed filters are evaluated in objective and subjective manners with both synthetic phantom and real clinical US images. It is shown that the proposed methods have better ability for noise reduction comparing with the other state-of-art de-speckling filters.

Published in:

Machine Vision and Image Processing (MVIP), 2011 7th Iranian

Date of Conference:

16-17 Nov. 2011