Enhanced Steerable Pyramid Transformation for Medical Ultrasound Image Despeckling

Abstract

The paper presents a novel approach for suppressing speckle noise at the same time preserving edge information effectively in ultrasound images for better clinical analysis and problem identification. The framework includes the modified adaptive Wiener filter (MAWF) along with the Canny edge detection method and enhanced steerable pyramid transformation (SPT) algorithm. The Canny algorithm is used to detect the true edges from the noisy ultrasound (US) image, and the MAWF algorithm smoothens the speckle effect without affecting the edge information which is preserved separately and added to the final output. The discrete Fourier transform (DFT) is used to extract the low and high frequency coefficients. Unlike other multiresolution techniques used for speckle suppression, the proposed method uses the steerable pyramid transformation technique based on high frequency components extracted using DFT for image enhancement. The coherence component extraction (CCE) method enhances the overall texture and edge features of the image even in the darker portions of the image. The output of this stage is finally combined with the stored edge information. This paper also presents experimental results to show that the proposed technique outperforms other state-of-art techniques in terms of peak signal to noise ratio, structural similarity index, and universal quality index.