Abstract
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.
Highlights
Ultrasound (US) imaging devices have been exploited to examine human bodies of various ages, from young to old people; US imaging is one of the most widely used imaging technologies in the medical diagnosis field
The speckle noise elimination and feature preservation performances of the conventional methods (Gaussian [31], Lee [6], Frost [8], anisotropic diffusion filter with memory based on speckle statistics (ADMSS) [47], speckle reducing anisotropic diffusion (SRAD) [31], weighted least squares (WLS) [48], guided image filtering (GIF) [43], Bitonic [49], SRAD-Bayes algorithm [44], and synthetic aperture radar block matching 3-D (SAR-BM3D) [50]), and the proposed algorithm were compared
The speckle noise elimination and feature preservation performances of the conventional methods (Gaussian [31], Lee [6], Frost [8], anisotropic diffusion filter with memory based on speckle statistics (ADMSS) [47], SRAD [31], weighted least squares (WLS) [48], GIF [43], Bitonic [49], SRAD-Bayes algorithm [44], and synthetic aperture radar block matching 3-D (SAR(d)
Summary
Ultrasound (US) imaging devices have been exploited to examine human bodies of various ages, from young to old people; US imaging is one of the most widely used imaging technologies in the medical diagnosis field. US imaging devices can be inexpensive, protected from radiation, and portable compared with other medical imaging devices such as X-ray imaging, computer tomography, magnetic resonance imaging, and positron emission tomography [1,2]. Another advantage is that it can produce a real-time image. Based on these merits, US imaging devices are widely utilized to diagnose lesions in muscles, joints, blood vessels, and internal organs. To gain a reliable lesion diagnosis and analysis through US imaging, a speckle noise suppression algorithm is an essential preprocessing technique
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