IJIRST –International Journal for Innovative Research in Science & Technology| Volume 4 | Issue 12 | May 2018 ISSN (online): 2349-6010 All rights reserved by www.ijirst.org 15 A Review on Speckle Noise Reduction in Ultrasound Images by Comparing Various Filters Poonam Chauhan Vikas Kaushik M. Tech. Scholar Assistant Professor Department of Electronics & Communication Engineering Department of Electronics & Communication Engineering & Department of Electrical & Electronics Engineering Global Research Institute of Management and Technology (GRIMT), RADAUR, YNR, Haryana Global Research Institute of Management and Technology (GRIMT), RADAUR, YNR, Haryana Abstract Speckle noise is a different kind of noise which mostly occurs in coherence imaging system like ultrasound images, lasers, sonar, synthetic aperture of radar etc. So, in ultrasound images, the presence of speckle noise results in decrement of quality of an image and due this noise it becomes difficult for human and doctors to diagnose. So it cannot be possible to interpret the change with naked eyes. It also becomes hard to differentiate between speckle noise and clinical information because speckle noise behaves like information. So it is necessary to overcome this problem by reducing noise. Many numerical methods are proposed for filtering of speckle noise or speckle suppression. This paper presents a comparative review of various speckle reduction filters. These filters have different results and behaviour. Similarly some filters do better coherence, edge enhancement and segmentation etc. In the end, it provides some commonly used filtering techniques for de-noising. Keywords: Speckle Noise, Ultrasound, Frost, Lee, Diffusion Tensor Anisotropic and Coherence _______________________________________________________________________________________________________ I. INTRODUCTION A noise occurrence is a major problem in bio-medical imaging technique. Each of these biomedical imaging devices is affected by different types of noise. For example, x-ray images corrupted by Poisson noise are very often but the ultrasound images are affected by the Speckle noise [1]. Speckle noise is in the form of black spots present in ultrasound images and mostly present in coherent images. Due to speckle noise presence it reduces the resolution of the ultrasound images mainly in images of low contrast .It may be difficult to explain automatic due to low SNR i.e (signal to noise ratio) in ultrasound images. This low SNR is mainly due to speckle noise present in the ultrasound image [2]. So it becomes necessary to de noising of ultrasound images. By using such technique of de noising with retaining the important features as it is [3]. It is one of the most widely used diagnostic tools in modern medical science. This technology is relatively cheap and portable, as when compared with other imaging techniques such as magnetic resonance imaging (MRI) and computed tomography (CT).It is a non-penetrating technique of imaging. It has no known long term side effects and rarely causes any discomfort to the patient [4]. It is a cheap and reliable technique. Many research work has been done to reduce the speckle noise in ultrasound images in past few years. Temporal averaging is the first technique with the help of which speckle noise in ultrasound images was reduced. Averaging of same scene multiple uncorrelated frames are to be done to reduce the noise effect in this technique. This temporal technique is very fast and simple, but it generate blurry image and some of the details are lost [2].There is an another filter also that has been proposed for speckle reduction, it uses the weights of surrounding pixels and the filter has been named as AWMF ie Adaptive Weighted Median Filter. In AWMF filter weighted median filter is used for suppressing the speckle noise presents in imaging system and this technique based on the variable weight coefficient around every pixel. ASSF i.e Adaptive Speckle Suppression has been proposed after AWMF filter. ASSF filter is also uses the same local statistics of ultrasound images. So every filter has some limitations. The optimized work is based on using diffusion anisotropic filter. II. DIFFUSION TENSOR Diffusion Tensor Imaging (DTI) has evolved into a primary technique for non-invasive characterization of the structure and architecture of living tissue [5]. In order to describe local variations structures presents in the image and to provide a fairly robust anisotropic diffusion, we use the diffusion tensor [6]. Such characterizations based on the Eigen parameters of the diffusion tensors measured, which includes assessment of tissue structural integrity by differences in the Eigen values and examination of architectural features by directions of the Eigen-vectors [5]. The tensor can be constructed in two ways, as a coherence-enhancing diffusion (CED) [6] or as edge-enhancing diffusion (EED). Recently the CED and EED algorithms were combined in hybrid diffusion filter with a continuous switch (HDCS). The diffusion tensor filtering equation is described by: