Blind Source Parameters for Performance Evaluation of Despeckling Filters
| Autor: | D. Nagashettappa Biradar, M. L. Dewal, Yogesh Gundge, D. Sanjaykumar Gowre, Manojkumar Rohit |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2016 |
| Předmět: |
lcsh:Medical physics. Medical radiology. Nuclear medicine
lcsh:Medical technology Mean squared error Article Subject Anisotropic diffusion Computer science lcsh:R895-920 0206 medical engineering 02 engineering and technology Speckle pattern Wavelet 0202 electrical engineering electronic engineering information engineering Radiology Nuclear Medicine and imaging Computer vision Bayes estimator Noise (signal processing) business.industry Speckle noise Pattern recognition Filter (signal processing) 020601 biomedical engineering lcsh:R855-855.5 020201 artificial intelligence & image processing Artificial intelligence business Research Article |
| Zdroj: | International Journal of Biomedical Imaging, Vol 2016 (2016) International Journal of Biomedical Imaging |
| ISSN: | 1687-4188 |
| DOI: | 10.1155/2016/3636017 |
| Popis: | The speckle noise is inherent to transthoracic echocardiographic images. A standard noise-free reference echocardiographic image does not exist. The evaluation of filters based on the traditional parameters such as peak signal-to-noise ratio, mean square error, and structural similarity index may not reflect the true filter performance on echocardiographic images. Therefore, the performance of despeckling can be evaluated using blind assessment metrics like the speckle suppression index, speckle suppression and mean preservation index (SMPI), and beta metric. The need for noise-free reference image is overcome using these three parameters. This paper presents a comprehensive analysis and evaluation of eleven types of despeckling filters for echocardiographic images in terms of blind and traditional performance parameters along with clinical validation. The noise is effectively suppressed using the logarithmic neighborhood shrinkage (NeighShrink) embedded with Stein’s unbiased risk estimation (SURE). The SMPI is three times more effective compared to the wavelet based generalized likelihood estimation approach. The quantitative evaluation and clinical validation reveal that the filters such as the nonlocal mean, posterior sampling based Bayesian estimation, hybrid median, and probabilistic patch based filters are acceptable whereas median, anisotropic diffusion, fuzzy, and Ripplet nonlinear approximation filters have limited applications for echocardiographic images. |
| Databáze: | OpenAIRE |
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