Abstract

Speckle noise in ultrasound (US) medical images is the prime factor that undermines its full utilization. This noise is added by the constructive / destructive interference of sound waves travelling through hard- and soft-tissues of a patient. It is therefore generally accepted that the noise is unavoidable. As an alternate researchers have proposed several algorithms to somewhat undermine the effect of speckle noise. The discrete wavelet transform (DWT) has been used by several researchers. However, the performance of only a few transforms has been demonstrated. This paper provides a comparison of several DWT. The algorithm comprises of a pre-processing stage using Wiener filter, and a post-processing stage using Median filter. The processed image is compared with the original image on four metrics: two are based on full-reference (FR) image quality assessment (IQA), and the remaining two are based on no-reference (NR) IQA metrics. The FR-IQA are peak signal-to-noise ratio (PSNR) and mean structurally similarity index measure (MSSIM). The two NR-IQA techniques are blind pseudo-reference image (BPRI), and blind multiple pseudo-reference images (BMPRI). It has been demonstrated that some of these wavelet transforms outperform others by a significant margin.

Highlights

  • An ultrasound (US) medical image helps in an early diagnosis of kidney stones

  • A quick comparison of the results reveal that a significant amount of speckle noise has been removed

  • This paper reviews the performance of seven discrete wavelet transforms in reducing the effect of speckle noise of the US medical images

Read more

Summary

INTRODUCTION

An ultrasound (US) medical image helps in an early diagnosis of kidney stones. These stones cause severe pain in situations where they become large or block the flow of urine. The practical issue of speckle noise is not new This has been addressed by several researchers, some as early as 1980‟s. The introduction of wavelets during the early 90‟s resulted in several papers on speckle noise. A comparison of five wavelets Haar, Daubechies, Symlet, Coiflet and biorthogonal www.ijacsa.thesai.org (IJACSA) International Journal of Advanced Computer Science and Applications, Vol 10, No 5, 2019 wavelets for removing the speckle noise has been given in [19]. This paper compares the performance of seven wavelets for reducing the speckle noise in US medical images.

EVALUATION CRITERIA
WAVELET SELECTION
SIMULATIONS
CONCLUSIONS
Full Text
Published version (Free)

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call