Abstract
This article proposes a technique for speckle reduction in medical ultrasound (US) imaging which preserves the point and linear features with the added advantage of energy condensation regulator. Whatever be the post processing task on US image, the image should undergo a preprocessing step called despeckling. Nowadays, though the US machines are available with built-in speckle reduction facility, they are suffered by many practical limitations such as limited dynamic range of the display, limited number of unique directions that an US beam scan follow to average an image and limited size of transducer, etc. The proposed diffusion model can be used as a visual enhancement tool for interpretation as well as a preprocessing task for further diagnosis. This method incorporates two terms: diffusion and regulator. The anisotropic diffusion preserves and enhances edges and local details. The regularization enables the correction of feature broadening distortion which is the common problem in second-order diffusion-based methods. In this scheme, the diffusion matrix is designed using local coordinate transformation and the feature broadening correction term is derived from energy function. Performance of the proposed method has been illustrated using synthetic and real US data. Experiments indicate better speckle reduction and effective preservation of edges and local details.
Highlights
For more than two decades, ultrasonography has been considered as one of the most powerful techniques for imaging organs and soft tissue structures in the human body
The performance of Perona and Malik diffusion (PM), adaptive weighted median filter (AWMF), speckle reducing anisotropic diffusion (SRAD), nonlinear coherent diffusion (NCD), median boosted anisotropic diffusion (MBAD), Laplacian pyramidbased nonlinear diffusion (LPND), and condensed anisotropic diffusion (CAD) methods are compared in terms of signal-to-noise ratio (SNR), structural similarity index (SSIM), and FOM
In this article, we propose a new diffusion model called CAD that reduces speckle, preserves information carrying features and avoids blocking effects, point, and linear feature broadening problems
Summary
For more than two decades, ultrasonography has been considered as one of the most powerful techniques for imaging organs and soft tissue structures in the human body. A new method has been proposed to reduce speckle in US images by incorporating a nonquadratic regularization into nonlinear coherent diffusion to preserve and enhance edges, local details, and to correct the feature broadening distortion. The proposed CAD model is composed of two components: the nonlinear coherent diffusion component and the energy condensation component The former accounts for speckle removal and the latter reduces the broadening distortion of point and linear features. The region corresponds to FFS carries less tissue information, i.e., small gradient variations and the diffusion must become isotropic along all directions, i.e., λ1 % λ2 This condition can be accomplished by setting the local coherence measured by μ1 − μ2 close to zero. The homogeneous region in the image exhibits less contrast after diffusion than compared to the original one
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