Study of speckle noise reduction from ultrasound B-mode images using different filtering techniques

TLDR: In this study, Wiener filter outperforms comparing to Median filter, Lee filter, Kuan filter, and Frost filtering techniques in terms of PSNR, MSE, RMSE, SNR, and MSSIM.

Abstract: Ultrasound imaging is one of the most popular noninvasive techniques for diagnosis than other imaging modalities like X-rays, MRI, and CT scans because of ultrasonic techniques are safe, cheaper and do not have ionizing radiation effects. The two-dimensional ultrasound B-mode image composed of bright and dark dots representing the ultrasound echoes. The problems of Ultrasound B-mode images are low resolution and speckle noise. Speckle noise consisting of weak scattering echoes returning from the interface of the object is a premier problem of ultrasound B-mode images. Recently, different techniques have been mentioned for speckle noise reduction. But filtering techniques are very popular as filters can effectively inhibit the noise and decrease the complexity. In this work, our objective is to study the speckle noise reduction of ultrasound B-mode image considering kidney, fetus, cyst and liver phantoms using different filtering techniques. Here, we have used the non-adaptive filter as like Median filter, and adaptive filter as Lee filter, Kuan filter, Frost filter, and Wiener filter for the reduction of speckle noise. The necessity of this study is to choose an effective technique for reducing speckle noise. Mean Square Error (MSE), Root Mean Square Error (RMSE), Peak Signal-to-noise ratio (PSNR), Signal-to-noise ratio (SNR), Normalized Absolute Error (NAE), and Mean Structure Similarity Index Map (MSSIM) are applied for analyzing the image attribute and assessing the performance. In this study, Wiener filter outperforms comparing to Median filter, Lee filter, Kuan filter, and Frost filtering techniques in terms of PSNR, MSE, RMSE, SNR, and MSSIM.