Despeckling of medical ultrasound images using data and rate adaptive lossy compression

Abstract

A novel technique for despeckling the medical ultrasound images using lossy compression is presented. The logarithm of the input image is first transformed to the multiscale wavelet domain. It is then shown that the subband coefficients of the log-transformed ultrasound image can be successfully modeled using the generalized Laplacian distribution. Based on this modeling, a simple adaptation of the zero-zone and reconstruction levels of the uniform threshold quantizer is proposed in order to achieve simultaneous despeckling and quantization. This adaptation is based on: (1) an estimate of the corrupting speckle noise level in the image; (2) the estimated statistics of the noise-free subband coefficients; and (3) the required compression rate. The Laplacian distribution is considered as a special case of the generalized Laplacian distribution and its efficacy is demonstrated for the problem under consideration. Context-based classification is also applied to the noisy coefficients to enhance the performance of the subband coder. Simulation results using a contrast detail phantom image and several real ultrasound images are presented. To validate the performance of the proposed scheme, comparison with two two-stage schemes, wherein the speckled image is first filtered and then compressed using the state-of-the-art JPEG2000 encoder, is presented. Experimental results show that the proposed scheme works better, both in terms of the signal to noise ratio and the visual quality.

Speckle Reduction in Echocardiography: Trends and Perceptions

“…This a priori information is used to infer the noise free coefficients. Gupta et al (Gupta et al, 2005) modelled the wavelet coefficients of the underlying speckle free image using a generalised Laplacian distribution, simultaneously removing speckle and performing compression using a quantisation function. This function adapts to the estimated level of speckle.…”

Speckle Noise Reduction in Breast Ultrasound Images for Segmentation of Region Of Interest (ROI) Using Discrete Wavelets

“…To improve the human interpretation and for the image processing tasks like segmentation and registration [7], speckle denoising is very essential. To reduce speckle noise, many techniques like Kuan filter [8], Frost filter [9], Speckle Reduction Anisotropic Diffusion (srad) [10], and Wavelet thresholding [11] have been used. Temporal averaging technique increases the signal-to-noise ratio (SNR) by averaging multiple uncorrelated images that are obtained by the transducer shift.…”

Performance Comparison of Wavelet Transform and Contourlet Transform based methods for Despeckling Medical Ultrasound Images

“…Nikhil Gupta and M.N. Swamy [7] have proposed a novel technique for despeckling the medical ultrasound images using lossy compression and wavelet transform. There has been growing awareness to the observation that wavelet transform is not capable of diagnosing the direction of any line-shaped discontinuity in the image [6].…”