Wavelet-based Despeckling of Medical Ultrasound Images

Abstract

This paper presents a new wavelet-domain technique for despeckling of medical ultrasound (US) images for improved clinical diagnosis. The method uses the generalized Gaussian distribution and generalized gamma distribution to model the image and the speckle, respectively, in the detailed sub-bands of wavelet decomposition of the log-transformed US image. Combining these, a priori distributions with the Bayesian maximum a posteriori criterion, shrinkage estimators are derived for processing the wavelet coefficients of the detail sub-bands. The visual comparison of despeckled US images and the higher values of quality metrics indicate that the new method suppresses the speckle noise well while preserving the texture and organ surfaces.

Keywords:

• Despeckling
• Generalized gamma distribution
• Maximum a posteriori estimator
• Shrinkage estimator
• Speckle noise
• Ultrasound image

Additional information

Notes on contributors

Bhabesh Deka

Bhabesh Deka received the B. Eng. degree in Electronics and Telecommunication from Gauhati University, Assam, India in 1999 and the M. Tech degree from Tezpur Central University, Assam, India in 2001. He received Ph. D. degree from Indian Institute of Technology Guwahati, India in 2011. Currently, he is an Associate Professor in the Department of Electronics and Communication Engineering, Tezpur Central University, Assam, India. His research interests include almost all aspects of image and signal processing with a special emphasis on image restoration and compressive sensing. E-mail: [email protected]

Prabin Kumar Bora

Prabin Kumar Bora received the B. Eng. degree in Electrical Engineering from Assam Engineering College, Guwahati, India, in 1984 and the M. Eng. and Ph. D. degrees in Electrical Engineering from the Indian Institute of Science, Bangalore, in 1990 and 1993, respectively. Currently, he is a Professor in the Department of Electronics and Electrical Engineering, Indian Institute of Technology, Guwahati. His research interests include video coding, image and video watermarking, perceptual video hashing, and computer vision. E-mail: [email protected]