Despeckling Algorithm for Removing Speckle Noise from Ultrasound Images
Ultrasound (US) imaging can examine human bodies of various ages; however, in the process of obtaining a US image, speckle noise is generated. The speckle noise inhibits physicians from accurately examining lesions; thus, a speckle noise removal method is essential technology. To enhance speckle noi...
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| Format: | Article |
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MDPI AG
2020-06-01
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| Series: | Symmetry |
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| Online Access: | https://www.mdpi.com/2073-8994/12/6/938 |
| _version_ | 1797566258699304960 |
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| author | Hyunho Choi Jechang Jeong |
| author_facet | Hyunho Choi Jechang Jeong |
| author_sort | Hyunho Choi |
| collection | DOAJ |
| description | Ultrasound (US) imaging can examine human bodies of various ages; however, in the process of obtaining a US image, speckle noise is generated. The speckle noise inhibits physicians from accurately examining lesions; thus, a speckle noise removal method is essential technology. To enhance speckle noise elimination, we propose a novel algorithm using the characteristics of speckle noise and filtering methods based on speckle reducing anisotropic diffusion (SRAD) filtering, discrete wavelet transform (DWT) using symmetry characteristics, weighted guided image filtering (WGIF), and gradient domain guided image filtering (GDGIF). The SRAD filter is exploited as a preprocessing filter because it can be directly applied to a medical US image containing speckle noise without a log-compression. The wavelet domain has the advantage of suppressing the additive noise. Therefore, a homomorphic transformation is utilized to convert the multiplicative noise into additive noise. After two-level DWT decomposition is applied, to suppress the residual noise of an SRAD filtered image, GDGIF and WGIF are exploited to reduce noise from seven high-frequency sub-band images and one low-frequency sub-band image, respectively. Finally, a noise-free image is attained through inverse DWT and an exponential transform. The proposed algorithm exhibits excellent speckle noise elimination and edge conservation as compared with conventional denoising methods. |
| first_indexed | 2024-03-10T19:24:12Z |
| format | Article |
| id | doaj.art-cebd01ce2b4746c2811327743d9662c7 |
| institution | Directory Open Access Journal |
| issn | 2073-8994 |
| language | English |
| last_indexed | 2024-03-10T19:24:12Z |
| publishDate | 2020-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Symmetry |
| spelling | doaj.art-cebd01ce2b4746c2811327743d9662c72023-11-20T02:42:24ZengMDPI AGSymmetry2073-89942020-06-0112693810.3390/sym12060938Despeckling Algorithm for Removing Speckle Noise from Ultrasound ImagesHyunho Choi0Jechang Jeong1Department of Electronics and Computer Engineering, Hanyang University, 222, Wangsimni-ro, Seongdong-gu, Seoul 04763, KoreaDepartment of Electronics and Computer Engineering, Hanyang University, 222, Wangsimni-ro, Seongdong-gu, Seoul 04763, KoreaUltrasound (US) imaging can examine human bodies of various ages; however, in the process of obtaining a US image, speckle noise is generated. The speckle noise inhibits physicians from accurately examining lesions; thus, a speckle noise removal method is essential technology. To enhance speckle noise elimination, we propose a novel algorithm using the characteristics of speckle noise and filtering methods based on speckle reducing anisotropic diffusion (SRAD) filtering, discrete wavelet transform (DWT) using symmetry characteristics, weighted guided image filtering (WGIF), and gradient domain guided image filtering (GDGIF). The SRAD filter is exploited as a preprocessing filter because it can be directly applied to a medical US image containing speckle noise without a log-compression. The wavelet domain has the advantage of suppressing the additive noise. Therefore, a homomorphic transformation is utilized to convert the multiplicative noise into additive noise. After two-level DWT decomposition is applied, to suppress the residual noise of an SRAD filtered image, GDGIF and WGIF are exploited to reduce noise from seven high-frequency sub-band images and one low-frequency sub-band image, respectively. Finally, a noise-free image is attained through inverse DWT and an exponential transform. The proposed algorithm exhibits excellent speckle noise elimination and edge conservation as compared with conventional denoising methods.https://www.mdpi.com/2073-8994/12/6/938ultrasound imagingdiscrete wavelet transformweighted guided image filteringgradient domain guided image filteringspeckle noise |
| spellingShingle | Hyunho Choi Jechang Jeong Despeckling Algorithm for Removing Speckle Noise from Ultrasound Images Symmetry ultrasound imaging discrete wavelet transform weighted guided image filtering gradient domain guided image filtering speckle noise |
| title | Despeckling Algorithm for Removing Speckle Noise from Ultrasound Images |
| title_full | Despeckling Algorithm for Removing Speckle Noise from Ultrasound Images |
| title_fullStr | Despeckling Algorithm for Removing Speckle Noise from Ultrasound Images |
| title_full_unstemmed | Despeckling Algorithm for Removing Speckle Noise from Ultrasound Images |
| title_short | Despeckling Algorithm for Removing Speckle Noise from Ultrasound Images |
| title_sort | despeckling algorithm for removing speckle noise from ultrasound images |
| topic | ultrasound imaging discrete wavelet transform weighted guided image filtering gradient domain guided image filtering speckle noise |
| url | https://www.mdpi.com/2073-8994/12/6/938 |
| work_keys_str_mv | AT hyunhochoi despecklingalgorithmforremovingspecklenoisefromultrasoundimages AT jechangjeong despecklingalgorithmforremovingspecklenoisefromultrasoundimages |