Comparison of Despeckle Filters for Breast Ultrasound Images
Abstract It is well known that the quality of ultrasound image is significantly degraded by the speckle noise, which has restricted the development of automatic diagnostic techniques for ultrasound images, especially for the breast ultrasound images. This necessitates the need to choose an optimal s...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Zhang, Ju [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2014 |
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| Schlagwörter: |
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| Anmerkung: |
© Springer Science+Business Media New York 2014 |
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| Übergeordnetes Werk: |
Enthalten in: Circuits, systems and signal processing - Boston, Mass. : Birkhäuser, 1982, 34(2014), 1 vom: 18. Juni, Seite 185-208 |
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| Übergeordnetes Werk: |
volume:34 ; year:2014 ; number:1 ; day:18 ; month:06 ; pages:185-208 |
| Links: |
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| DOI / URN: |
10.1007/s00034-014-9829-y |
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SPR000522155 |
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| 520 | |a Abstract It is well known that the quality of ultrasound image is significantly degraded by the speckle noise, which has restricted the development of automatic diagnostic techniques for ultrasound images, especially for the breast ultrasound images. This necessitates the need to choose an optimal speckle filtering algorithm for the specific clinical application with different required criteria. In this paper, the study focuses on the comparison of despeckle filters for the breast ultrasound images. Firstly, the models of speckle noise for medical ultrasound images are discussed. After that, eleven despeckle filters which are classified into five categories (local adaptive filter, anisotropic diffusion filter, multi-scale filter, nonlocal means filter, and hybrid filter) are described. Then, the comparative experiments of eleven despeckle filters for the two types of simulated images and clinical ultrasound breast images are presented. Finally, to objectively and systematically compare the performance of eleven despeckle filters, several comparison methods are used, such as the full-reference image quality metrics, the nonreference/blind image quality metrics, observing the removed noise images, as well as the visual evaluation of experts. | ||
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| 650 | 4 | |a Ultrasound image |7 (dpeaa)DE-He213 | |
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| 650 | 4 | |a Image quality metrics |7 (dpeaa)DE-He213 | |
| 700 | 1 | |a Wang, Chen |4 aut | |
| 700 | 1 | |a Cheng, Yun |4 aut | |
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10.1007/s00034-014-9829-y doi (DE-627)SPR000522155 (SPR)s00034-014-9829-y-e DE-627 ger DE-627 rakwb eng Zhang, Ju verfasserin aut Comparison of Despeckle Filters for Breast Ultrasound Images 2014 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer Science+Business Media New York 2014 Abstract It is well known that the quality of ultrasound image is significantly degraded by the speckle noise, which has restricted the development of automatic diagnostic techniques for ultrasound images, especially for the breast ultrasound images. This necessitates the need to choose an optimal speckle filtering algorithm for the specific clinical application with different required criteria. In this paper, the study focuses on the comparison of despeckle filters for the breast ultrasound images. Firstly, the models of speckle noise for medical ultrasound images are discussed. After that, eleven despeckle filters which are classified into five categories (local adaptive filter, anisotropic diffusion filter, multi-scale filter, nonlocal means filter, and hybrid filter) are described. Then, the comparative experiments of eleven despeckle filters for the two types of simulated images and clinical ultrasound breast images are presented. Finally, to objectively and systematically compare the performance of eleven despeckle filters, several comparison methods are used, such as the full-reference image quality metrics, the nonreference/blind image quality metrics, observing the removed noise images, as well as the visual evaluation of experts. Despeckle comparison (dpeaa)DE-He213 Ultrasound image (dpeaa)DE-He213 Speckle noise (dpeaa)DE-He213 Image quality metrics (dpeaa)DE-He213 Wang, Chen aut Cheng, Yun aut Enthalten in Circuits, systems and signal processing Boston, Mass. : Birkhäuser, 1982 34(2014), 1 vom: 18. Juni, Seite 185-208 (DE-627)351975470 (DE-600)2085136-4 1531-5878 nnns volume:34 year:2014 number:1 day:18 month:06 pages:185-208 https://dx.doi.org/10.1007/s00034-014-9829-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 34 2014 1 18 06 185-208 |
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10.1007/s00034-014-9829-y doi (DE-627)SPR000522155 (SPR)s00034-014-9829-y-e DE-627 ger DE-627 rakwb eng Zhang, Ju verfasserin aut Comparison of Despeckle Filters for Breast Ultrasound Images 2014 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer Science+Business Media New York 2014 Abstract It is well known that the quality of ultrasound image is significantly degraded by the speckle noise, which has restricted the development of automatic diagnostic techniques for ultrasound images, especially for the breast ultrasound images. This necessitates the need to choose an optimal speckle filtering algorithm for the specific clinical application with different required criteria. In this paper, the study focuses on the comparison of despeckle filters for the breast ultrasound images. Firstly, the models of speckle noise for medical ultrasound images are discussed. After that, eleven despeckle filters which are classified into five categories (local adaptive filter, anisotropic diffusion filter, multi-scale filter, nonlocal means filter, and hybrid filter) are described. Then, the comparative experiments of eleven despeckle filters for the two types of simulated images and clinical ultrasound breast images are presented. Finally, to objectively and systematically compare the performance of eleven despeckle filters, several comparison methods are used, such as the full-reference image quality metrics, the nonreference/blind image quality metrics, observing the removed noise images, as well as the visual evaluation of experts. Despeckle comparison (dpeaa)DE-He213 Ultrasound image (dpeaa)DE-He213 Speckle noise (dpeaa)DE-He213 Image quality metrics (dpeaa)DE-He213 Wang, Chen aut Cheng, Yun aut Enthalten in Circuits, systems and signal processing Boston, Mass. : Birkhäuser, 1982 34(2014), 1 vom: 18. Juni, Seite 185-208 (DE-627)351975470 (DE-600)2085136-4 1531-5878 nnns volume:34 year:2014 number:1 day:18 month:06 pages:185-208 https://dx.doi.org/10.1007/s00034-014-9829-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 34 2014 1 18 06 185-208 |
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10.1007/s00034-014-9829-y doi (DE-627)SPR000522155 (SPR)s00034-014-9829-y-e DE-627 ger DE-627 rakwb eng Zhang, Ju verfasserin aut Comparison of Despeckle Filters for Breast Ultrasound Images 2014 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer Science+Business Media New York 2014 Abstract It is well known that the quality of ultrasound image is significantly degraded by the speckle noise, which has restricted the development of automatic diagnostic techniques for ultrasound images, especially for the breast ultrasound images. This necessitates the need to choose an optimal speckle filtering algorithm for the specific clinical application with different required criteria. In this paper, the study focuses on the comparison of despeckle filters for the breast ultrasound images. Firstly, the models of speckle noise for medical ultrasound images are discussed. After that, eleven despeckle filters which are classified into five categories (local adaptive filter, anisotropic diffusion filter, multi-scale filter, nonlocal means filter, and hybrid filter) are described. Then, the comparative experiments of eleven despeckle filters for the two types of simulated images and clinical ultrasound breast images are presented. Finally, to objectively and systematically compare the performance of eleven despeckle filters, several comparison methods are used, such as the full-reference image quality metrics, the nonreference/blind image quality metrics, observing the removed noise images, as well as the visual evaluation of experts. Despeckle comparison (dpeaa)DE-He213 Ultrasound image (dpeaa)DE-He213 Speckle noise (dpeaa)DE-He213 Image quality metrics (dpeaa)DE-He213 Wang, Chen aut Cheng, Yun aut Enthalten in Circuits, systems and signal processing Boston, Mass. : Birkhäuser, 1982 34(2014), 1 vom: 18. Juni, Seite 185-208 (DE-627)351975470 (DE-600)2085136-4 1531-5878 nnns volume:34 year:2014 number:1 day:18 month:06 pages:185-208 https://dx.doi.org/10.1007/s00034-014-9829-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 34 2014 1 18 06 185-208 |
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10.1007/s00034-014-9829-y doi (DE-627)SPR000522155 (SPR)s00034-014-9829-y-e DE-627 ger DE-627 rakwb eng Zhang, Ju verfasserin aut Comparison of Despeckle Filters for Breast Ultrasound Images 2014 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer Science+Business Media New York 2014 Abstract It is well known that the quality of ultrasound image is significantly degraded by the speckle noise, which has restricted the development of automatic diagnostic techniques for ultrasound images, especially for the breast ultrasound images. This necessitates the need to choose an optimal speckle filtering algorithm for the specific clinical application with different required criteria. In this paper, the study focuses on the comparison of despeckle filters for the breast ultrasound images. Firstly, the models of speckle noise for medical ultrasound images are discussed. After that, eleven despeckle filters which are classified into five categories (local adaptive filter, anisotropic diffusion filter, multi-scale filter, nonlocal means filter, and hybrid filter) are described. Then, the comparative experiments of eleven despeckle filters for the two types of simulated images and clinical ultrasound breast images are presented. Finally, to objectively and systematically compare the performance of eleven despeckle filters, several comparison methods are used, such as the full-reference image quality metrics, the nonreference/blind image quality metrics, observing the removed noise images, as well as the visual evaluation of experts. Despeckle comparison (dpeaa)DE-He213 Ultrasound image (dpeaa)DE-He213 Speckle noise (dpeaa)DE-He213 Image quality metrics (dpeaa)DE-He213 Wang, Chen aut Cheng, Yun aut Enthalten in Circuits, systems and signal processing Boston, Mass. : Birkhäuser, 1982 34(2014), 1 vom: 18. Juni, Seite 185-208 (DE-627)351975470 (DE-600)2085136-4 1531-5878 nnns volume:34 year:2014 number:1 day:18 month:06 pages:185-208 https://dx.doi.org/10.1007/s00034-014-9829-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 34 2014 1 18 06 185-208 |
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10.1007/s00034-014-9829-y doi (DE-627)SPR000522155 (SPR)s00034-014-9829-y-e DE-627 ger DE-627 rakwb eng Zhang, Ju verfasserin aut Comparison of Despeckle Filters for Breast Ultrasound Images 2014 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer Science+Business Media New York 2014 Abstract It is well known that the quality of ultrasound image is significantly degraded by the speckle noise, which has restricted the development of automatic diagnostic techniques for ultrasound images, especially for the breast ultrasound images. This necessitates the need to choose an optimal speckle filtering algorithm for the specific clinical application with different required criteria. In this paper, the study focuses on the comparison of despeckle filters for the breast ultrasound images. Firstly, the models of speckle noise for medical ultrasound images are discussed. After that, eleven despeckle filters which are classified into five categories (local adaptive filter, anisotropic diffusion filter, multi-scale filter, nonlocal means filter, and hybrid filter) are described. Then, the comparative experiments of eleven despeckle filters for the two types of simulated images and clinical ultrasound breast images are presented. Finally, to objectively and systematically compare the performance of eleven despeckle filters, several comparison methods are used, such as the full-reference image quality metrics, the nonreference/blind image quality metrics, observing the removed noise images, as well as the visual evaluation of experts. Despeckle comparison (dpeaa)DE-He213 Ultrasound image (dpeaa)DE-He213 Speckle noise (dpeaa)DE-He213 Image quality metrics (dpeaa)DE-He213 Wang, Chen aut Cheng, Yun aut Enthalten in Circuits, systems and signal processing Boston, Mass. : Birkhäuser, 1982 34(2014), 1 vom: 18. Juni, Seite 185-208 (DE-627)351975470 (DE-600)2085136-4 1531-5878 nnns volume:34 year:2014 number:1 day:18 month:06 pages:185-208 https://dx.doi.org/10.1007/s00034-014-9829-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 34 2014 1 18 06 185-208 |
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Zhang, Ju |
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Zhang, Ju misc Despeckle comparison misc Ultrasound image misc Speckle noise misc Image quality metrics Comparison of Despeckle Filters for Breast Ultrasound Images |
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Comparison of Despeckle Filters for Breast Ultrasound Images Despeckle comparison (dpeaa)DE-He213 Ultrasound image (dpeaa)DE-He213 Speckle noise (dpeaa)DE-He213 Image quality metrics (dpeaa)DE-He213 |
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Comparison of Despeckle Filters for Breast Ultrasound Images |
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Comparison of Despeckle Filters for Breast Ultrasound Images |
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comparison of despeckle filters for breast ultrasound images |
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Comparison of Despeckle Filters for Breast Ultrasound Images |
| abstract |
Abstract It is well known that the quality of ultrasound image is significantly degraded by the speckle noise, which has restricted the development of automatic diagnostic techniques for ultrasound images, especially for the breast ultrasound images. This necessitates the need to choose an optimal speckle filtering algorithm for the specific clinical application with different required criteria. In this paper, the study focuses on the comparison of despeckle filters for the breast ultrasound images. Firstly, the models of speckle noise for medical ultrasound images are discussed. After that, eleven despeckle filters which are classified into five categories (local adaptive filter, anisotropic diffusion filter, multi-scale filter, nonlocal means filter, and hybrid filter) are described. Then, the comparative experiments of eleven despeckle filters for the two types of simulated images and clinical ultrasound breast images are presented. Finally, to objectively and systematically compare the performance of eleven despeckle filters, several comparison methods are used, such as the full-reference image quality metrics, the nonreference/blind image quality metrics, observing the removed noise images, as well as the visual evaluation of experts. © Springer Science+Business Media New York 2014 |
| abstractGer |
Abstract It is well known that the quality of ultrasound image is significantly degraded by the speckle noise, which has restricted the development of automatic diagnostic techniques for ultrasound images, especially for the breast ultrasound images. This necessitates the need to choose an optimal speckle filtering algorithm for the specific clinical application with different required criteria. In this paper, the study focuses on the comparison of despeckle filters for the breast ultrasound images. Firstly, the models of speckle noise for medical ultrasound images are discussed. After that, eleven despeckle filters which are classified into five categories (local adaptive filter, anisotropic diffusion filter, multi-scale filter, nonlocal means filter, and hybrid filter) are described. Then, the comparative experiments of eleven despeckle filters for the two types of simulated images and clinical ultrasound breast images are presented. Finally, to objectively and systematically compare the performance of eleven despeckle filters, several comparison methods are used, such as the full-reference image quality metrics, the nonreference/blind image quality metrics, observing the removed noise images, as well as the visual evaluation of experts. © Springer Science+Business Media New York 2014 |
| abstract_unstemmed |
Abstract It is well known that the quality of ultrasound image is significantly degraded by the speckle noise, which has restricted the development of automatic diagnostic techniques for ultrasound images, especially for the breast ultrasound images. This necessitates the need to choose an optimal speckle filtering algorithm for the specific clinical application with different required criteria. In this paper, the study focuses on the comparison of despeckle filters for the breast ultrasound images. Firstly, the models of speckle noise for medical ultrasound images are discussed. After that, eleven despeckle filters which are classified into five categories (local adaptive filter, anisotropic diffusion filter, multi-scale filter, nonlocal means filter, and hybrid filter) are described. Then, the comparative experiments of eleven despeckle filters for the two types of simulated images and clinical ultrasound breast images are presented. Finally, to objectively and systematically compare the performance of eleven despeckle filters, several comparison methods are used, such as the full-reference image quality metrics, the nonreference/blind image quality metrics, observing the removed noise images, as well as the visual evaluation of experts. © Springer Science+Business Media New York 2014 |
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Comparison of Despeckle Filters for Breast Ultrasound Images |
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Wang, Chen Cheng, Yun |
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10.1007/s00034-014-9829-y |
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