A MODIFIED ADAPTIVE WAVELET SHRINKAGE SPECKLE FILTER FOR ULTRASOUND IMAGES

Periyasamy Periyasamy

doi:10.3844/jcssp.2013.1406.1413

Abstract

The usage of ultrasound imaging for medical diagnosis is limited due to the presence of speckle noise. In this study Modified Adaptive Wavelet Shrinkage Filter (MAWSF) in the translational invariant domain is proposed for the removal of speckle noise. The adaptive wavelet threshold function removes the fixed bias of soft thresholding. A new inter-scale dependency model is proposed, to perform a primary clustering of signal of interest and noise. Then, anew sub-band adaptive threshold is determined for all high frequency sub-bands at various decomposition levels, to shrink the noisy coefficients. Experiment is conducted on several ultrasound scan images. The results show that this method yields better visual quality and Peak Signal to Noise Ratio (PSNR). Improvement in preservation of edge details is also found measured with Edge Preservation Index (EPI) measure.

Highlights

Denoising is (i) to remove the unwanted noise present in the images (ii) to preserve the edges and fine details (iii) to Digital Images are contaminated by noise during improve the visual quality of the images
In this study Modified Adaptive Wavelet Shrinkage Filter (MAWSF) in the translational invariant domain is proposed for the removal of speckle noise
The results show that this method yields better visual quality and Peak Signal to Noise Ratio (PSNR)

Summary

Introduction

Denoising is (i) to remove the unwanted noise present in the images (ii) to preserve the edges and fine details (iii) to Digital Images are contaminated by noise during improve the visual quality of the images. Synthetic Aperture Radar 2011) and references therein show a significant use of (SAR), Medical Ultrasound images are said to contain wavelet transform for denoising, called non linear speckle noise. Wavelet denoising attempts to remove noise constructive-destructive interference of the coherent and preserve the signal details irrespective of its ultrasound pulses that are backscattered from the tiny frequency content. In are reflected from the biological tissues, give rise to a granular pattern in the imaging data. This may reduce the contrast and resolution of the ultrasound image and it becomes a tedious process for the physician to make a diagnosis.

Methods

Results

Discussion

Conclusion