Ultrasound Image Despeckling Using Stochastic Distance-Based BM3D
| Autor: | Cid A. N. Santos, Nelson D. A. Mascarenhas, Diego L. N. Martins |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
Stochastic process
business.industry 0211 other engineering and technologies Pattern recognition 02 engineering and technology Computer Graphics and Computer-Aided Design Distance measures Euclidean distance symbols.namesake Speckle pattern Additive white Gaussian noise 0202 electrical engineering electronic engineering information engineering Medical imaging Collaborative filtering symbols 020201 artificial intelligence & image processing Computer vision Artificial intelligence business Software 021101 geological & geomatics engineering Interpretability Mathematics |
| Zdroj: | IEEE Transactions on Image Processing. 26:2632-2643 |
| ISSN: | 1941-0042 1057-7149 |
| DOI: | 10.1109/tip.2017.2685339 |
| Popis: | Ultrasound image despeckling is an important research field, since it can improve the interpretability of one of the main categories of medical imaging. Many techniques have been tried over the years for ultrasound despeckling, and more recently, a great deal of attention has been focused on patch-based methods, such as non-local means and block-matching collaborative filtering (BM3D). A common idea in these recent methods is the measure of distance between patches, originally proposed as the Euclidean distance, for filtering additive white Gaussian noise. In this paper, we derive new stochastic distances for the Fisher-Tippett distribution, based on well-known statistical divergences, and use them as patch distance measures in a modified version of the BM3D algorithm for despeckling log-compressed ultrasound images. State-of-the-art results in filtering simulated, synthetic, and real ultrasound images confirm the potential of the proposed approach. |
| Databáze: | OpenAIRE |
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