A Technique to Suppress Speckle in Ultrasound Images using Nonlocal Mean and Cellular Automata

Abstract

Kidney ultrasound imaging is an economical, non invasive, real time diagnosis system which is used to measure abnormalities in the shape, size and location of the kidneys in the body. The presence of speckle noise degrades the visual quality of ultrasound images. The primary objective of the research under taken is to detect the renal stone in ultrasound images. The present research paper discusses an algorithm based on cellular automata and nonlocal mean for suppression of the speckle noise. The cellular automatons are dynamic systems to represent the particular problem in terms of specific pattern. This concept is exploited to distinguish the noise from the object being target. The kidney ultrasound images are denoised to detect presence of renal stone in the image. The concept behind the nonlocal mean is an assumption that the pixel being considered for denosing has strong connection with the surrounding area rather than the exactly nearly by pixels. The MLNC (Maximum Likelihood Neighborhood Computation) rules are applied to find the nearest value in the neighbour. The BEP (Border, Edge Preservation) rules are used to safeguard the edges between the regions. The samples of 55 clinical images of ultrasound are used for statistical analysis which is carried out by Signal to Noise Ratio (SNR), Mean Square Error (MSE), Peak Signal to Noise Ratio (PSNR). The result analysis shows MSE = 216.789, SNR = 49.1569 and PSNR = 68.022. The algorithm not only enhances the visual quality but also suppress unwanted speckle noise. The algorithm is dynamic in nature and can be used to de-noise SAR images, MRI and X-rays image. The method is adaptive in nature as cellular modeling can model low noise level.