Speckle noise reduction in ultrasound images using a discrete wavelet transform-based image fusion technique

Choi, Hyun-Ho; Lee, Ju Hwan; Kim, Sung Min; Park, Sung Yun

doi:10.3233/bme-151458

Cited by 28 publications

(9 citation statements)

References 17 publications

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“…Some methods used to eliminate the speckle noise include adaptive mean filter and diffusion filters. The approximation component of the wavelet transform is another suitable method for noise elimination [19,20,37,38]. • Wavelet transform: Fourier transform of a signal only determines which frequencies are included in the initial signal, but does not indicate their occurrence time.…”

Section: Findings and Discussionmentioning

confidence: 99%

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2018

JCRC

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Introduction: Fatty liver is usually diagnosed by ultrasound, but this diagnosis can be difficult because the disease does not always lead to abnormal conditions on gray levels that can be detected by the eye. However, ultrasound is still the first choice to detect fatty liver due to its low cost and availability, and the lack of side effects. The study reviewed Computer-Aided Diagnosis approaches to fatty liver disease, based on wavelet transform sonographic image processing. Methods: In this review study, a search was conducted based on related keywords and articles that had been published in English over the last 12 years. The findings were extracted based on the aim of study. Findings: Nowadays wavelet transformation has been widely used in the field of medical image processing because of its adaptability to the characteristics of the human eye system. The well-known wavelets used to liver diseases detection include Haar, Symlet, Daubechies and Gabor. Extracting the proper properties of images plays an important role in detecting diseases. Important statistical features of image textures are: statistical descriptors based on the intensity histogram and the GLCM matrix (Gray level Co-occurrence Matrix). The popular algorithms used for liver disease include neural network, Support Vector Machine (SVM), Bayesian, decision tree, K-Nearest Neighbor (KNN), and regression. Conclusion: The sensitivity, specificity and accuracy of the extracted statistical features of the output components of wavelet transform are generally better than those obtained from the original image itself. Gabor's wavelet transformation often has a higher efficiency than the Daubechies and Symlet wavelet transforms because the two transforms only break up the halfband of low frequencies and lose some of the intermediate frequency regions, while Gabor retains all of the frequency regions This precision also mainly depends on the type of features selected and the type of classification. Statistical features based on intensity histograms do not provide relative information about the spatial of pixels relative to each other. To enter this spatial information of pixels in a texture analysis, it is recommended to use GLCM matrix in gray images. The type of classifier used can significantly impact on the precision of the final diagnosis.

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Section: Findings and Discussionmentioning

confidence: 99%

Untitled

2018

JCRC

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Section: Findings and Discussionmentioning

confidence: 99%

Computer-Aided Diagnosis Approaches to Fatty Liver Disease According to Sonographic Images Based on Wavelet Transform: A Review Study

2018

JCRC

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Introduction: Fatty liver is usually diagnosed by ultrasound, but this diagnosis can be difficult because the disease does not always lead to abnormal conditions on gray levels that can be detected by the eye. However, ultrasound is still the first choice to detect fatty liver due to its low cost and availability, and the lack of side effects. The study reviewed Computer-Aided Diagnosis approaches to fatty liver disease, based on wavelet transform sonographic image processing. Methods: In this review study, a search was conducted based on related keywords and articles that had been published in English over the last 12 years. The findings were extracted based on the aim of study. Findings: Nowadays wavelet transformation has been widely used in the field of medical image processing because of its adaptability to the characteristics of the human eye system. The well-known wavelets used to liver diseases detection include Haar, Symlet, Daubechies and Gabor. Extracting the proper properties of images plays an important role in detecting diseases. Important statistical features of image textures are: statistical descriptors based on the intensity histogram and the GLCM matrix (Gray level Co-occurrence Matrix). The popular algorithms used for liver disease include neural network, Support Vector Machine (SVM), Bayesian, decision tree, K-Nearest Neighbor (KNN), and regression. Conclusion: The sensitivity, specificity and accuracy of the extracted statistical features of the output components of wavelet transform are generally better than those obtained from the original image itself. Gabor’s wavelet transformation often has a higher efficiency than the Daubechies and Symlet wavelet transforms because the two transforms only break up the halfband of low frequencies and lose some of the intermediate frequency regions, while Gabor retains all of the frequency regions This precision also mainly depends on the type of features selected and the type of classification. Statistical features based on intensity histograms do not provide relative information about the spatial of pixels relative to each other. To enter this spatial information of pixels in a texture analysis, it is recommended to use GLCM matrix in gray images. The type of classifier used can significantly impact on the precision of the final diagnosis.

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“…An effect of algorithm and analysis of results is tested at one to two decomposition level. The two-level decomposition of the DWT shows the best results [42]. Most of the speckle noise occurs in high-frequency sub-band images [43].…”

Section: Proposed Algorithmmentioning

confidence: 99%

Speckle Noise Reduction Technique for SAR Images Using Statistical Characteristics of Speckle Noise and Discrete Wavelet Transform

Choi

Jeong

2019

Remote Sensing

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Synthetic aperture radar (SAR) images map Earth’s surface at high resolution, regardless of the weather conditions or sunshine phenomena. Therefore, SAR images have applications in various fields. Speckle noise, which has the characteristic of multiplicative noise, degrades the image quality of SAR images, which causes information loss. This study proposes a speckle noise reduction algorithm while using the speckle reducing anisotropic diffusion (SRAD) filter, discrete wavelet transform (DWT), soft threshold, improved guided filter (IGF), and guided filter (GF), with the aim of removing speckle noise. First, the SRAD filter is applied to the SAR images, and a logarithmic transform is used to convert multiplicative noise in the resulting SRAD image into additive noise. A two-level DWT is used to divide the resulting SRAD image into one low-frequency and six high-frequency sub-band images. To remove the additive noise and preserve edge information, horizontal and vertical sub-band images employ the soft threshold; the diagonal sub-band images employ the IGF; while, the low- frequency sub-band image removes additive noise using the GF. The experiments used both standard and real SAR images. The experimental results reveal that the proposed method, in comparison to state-of-the art methods, obtains excellent speckle noise removal, while preserving the edges and maintaining low computational complexity.

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Speckle noise reduction in ultrasound images using a discrete wavelet transform-based image fusion technique

Cited by 28 publications

References 17 publications

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Computer-Aided Diagnosis Approaches to Fatty Liver Disease According to Sonographic Images Based on Wavelet Transform: A Review Study

Speckle Noise Reduction Technique for SAR Images Using Statistical Characteristics of Speckle Noise and Discrete Wavelet Transform

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