Blind Source Parameters for Performance Evaluation of Despeckling Filters
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 no...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2016-01-01
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| Series: | International Journal of Biomedical Imaging |
| Online Access: | http://dx.doi.org/10.1155/2016/3636017 |
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| author | Nagashettappa Biradar M. L. Dewal ManojKumar Rohit Sanjaykumar Gowre Yogesh Gundge |
| author_facet | Nagashettappa Biradar M. L. Dewal ManojKumar Rohit Sanjaykumar Gowre Yogesh Gundge |
| author_sort | Nagashettappa Biradar |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-2153f175cc3f4a3da0aa1af5d4bc23ab |
| institution | Kabale University |
| issn | 1687-4188 1687-4196 |
| language | English |
| publishDate | 2016-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Biomedical Imaging |
| spelling | doaj-art-2153f175cc3f4a3da0aa1af5d4bc23ab2025-02-03T01:02:28ZengWileyInternational Journal of Biomedical Imaging1687-41881687-41962016-01-01201610.1155/2016/36360173636017Blind Source Parameters for Performance Evaluation of Despeckling FiltersNagashettappa Biradar0M. L. Dewal1ManojKumar Rohit2Sanjaykumar Gowre3Yogesh Gundge4Bheemanna Khandre Institute of Technology, Bhalki 58532, IndiaIndian Institute of Technology Roorkee, Roorkee 247667, IndiaPostgraduate Institute of Medical Education and Research, Chandigarh 160 012, IndiaBheemanna Khandre Institute of Technology, Bhalki 58532, IndiaBheemanna Khandre Institute of Technology, Bhalki 58532, IndiaThe 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.http://dx.doi.org/10.1155/2016/3636017 |
| spellingShingle | Nagashettappa Biradar M. L. Dewal ManojKumar Rohit Sanjaykumar Gowre Yogesh Gundge Blind Source Parameters for Performance Evaluation of Despeckling Filters International Journal of Biomedical Imaging |
| title | Blind Source Parameters for Performance Evaluation of Despeckling Filters |
| title_full | Blind Source Parameters for Performance Evaluation of Despeckling Filters |
| title_fullStr | Blind Source Parameters for Performance Evaluation of Despeckling Filters |
| title_full_unstemmed | Blind Source Parameters for Performance Evaluation of Despeckling Filters |
| title_short | Blind Source Parameters for Performance Evaluation of Despeckling Filters |
| title_sort | blind source parameters for performance evaluation of despeckling filters |
| url | http://dx.doi.org/10.1155/2016/3636017 |
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