A Novel Method for Speckle Reduction and Edge Enhancement in Ultrasonic Images

This paper presents a novel method for speckle reduction in ultrasonic images. Firstly, a particular filtering kernel is defined by decomposing the local rectangular neighborhood into asymmetric sticks pointing outside with variable orientation from the investigated pixel. Then the local mean and variance along each stick are calculated using a template based convolution algorithm. Finally, a pseudo-diffusion model is derived to diffuse the intensity averages of sticks into the central pixel, and a variance sensitive conductance functions is designed to adaptively control the diffusion strength in varying directions. The proposed method is in essence an integration of the linear boundary detection operator, i.e. stick technique, and the nonlinear diffusion model. In homogeneous regions, our method will act as a Gaussian like low pass filter, since the sticks are partially overlapped near the center, which implicitly assigns distance dependent weights to neighboring pixels. In heterogeneous regions, the information is expressed as many structures, which often occur as line boundaries or tube shapes in ultrasonic images, then our approach can encourage smoothing along the sticks falling inside the structures, and penalize blurring along the sticks across edges. The performance of our method is verified in experiments of both synthetic and clinical ultrasonic images. The results show that our method outperforms the existed filtering techniques in term of smoothing homogeneous regions, preserving resolvable features, enhancing weak edges and linear structures