# Systematic Evaluation on Speckle Suppression Methods in Examination of Ultrasound Breast Images
###### Abstract  
Breast ultrasound is an important tool used in the medical treatment and diagnosis of breast tumor. However, noise defined as speckles are generated inevitably. Although the existence of speckle may be beneficial to diagnosis if used by a well-trained observer, it often causes disturbance which negatively affects clinical diagnosis, not only by reducing resolution and contrast of ultrasound images, but also by adding difficulties to recognize tumor region accurately. In this paper, we investigate a number of popular de-speckling algorithms, including filters based on frequency domain, filters based on local statistical properties, filters based on minimum mean square error (MMSE), and filters based on Partial Differential Equation (PDE). Two visual measurement evaluation criteria, i.e., Mean to Variance Ratio (VMR) and Laplace Response of Domain (LRD), are chosen for the performance comparison of those filters in the application of ultrasound breast image filtering. Moreover, the filtering effect is further evaluated with respect to the segmentation accuracy of tumor regions. According to the evaluation results, we conclude that Bilateral Filter (BF) achieves the best visual effect. Although Weickert J Diffusion (WJD) and Total Variation (TV) can also obtain good visual effect and segmentation accuracy, they are very time-consuming. breast tumor; speckle suppression; comparison; ultrasound image +
Footnote †: journal: Medical  
region and clearer details in the edge region) is a prerequisite procedure in BUS image processing to promote image quality and tumor segmentation accuracy. Predecessors have made efforts to analyze the speckles in medical ultrasound images. Speckle pattern is categorized in three classes according to the number of scatterers per resolution cell called scatterer number density (SND). The first one is fully formed speckle (FFS) pattern. The SND of this speckle pattern is larger than 10, and the amplitude of the backscattered signal can be modeled as a Rayleigh (Rayleigh distribution) [5]. Moreover, the Speckle is a form of multiplicative noise triggered by the interference between ultrasound waves [6]. The second one is non-randomly distributed with long-range (NRLR) order [2], which can be modeled by K-distribution or Nakagami distribution. The third one is non-randomly distributed with short-range (NRSR) order [2], and the probability density function (PDF) of the backscattered signal becomes close to Rician distribution. The three classes [7] are associated with a constant Signal Noise Ratio (SNR) of 1.92, below 1.92 [8] and above 1.92, respectively. Generally, we assume that in some cases, properties of speckle in BUS images are similar to FFS [5] in which the contaminated image model can be expressed as the original one with multiplicative noise. Additionally, image inevitably contains additive Gaussian white noise (GWN) due to the inherent characteristics of communication channel. Therefore, the model of a contaminated image approximately expressed as: \(I_{g}=I_{f}\cdot n_{m}+n_{a}\)[4], where \(I_{f}\) stands for original image, \(I_{g}\) is the contaminated image, \(n_{m}\) is the multiplicative noise and is assumed to obey Generalized Gamma distribution [9], and \(n_{a}\) is the additive GWN. Note that \(n_{m}\) and \(n_{a}\), which form speckles, can be regarded as degradation function and additive noise, respectively. Various filtering algorithms have been applied to medical BUS images and achieved good performance for speckle suppression. The existing filtering algorithms can be classified into the following categories: (1) filters based on frequency domain; (2) filters based on the local statistical properties; (3) filters based on the minimum mean square error (MMSE); and (4) filters based on Partial Differential Equation (PDE). The filtering algorithms based on the information of spectrum can be labeled as filters based on the characteristics of frequency domain. They are applied to de-speckling by inhibiting a region of the spectrum.