Despeckling of medical ultrasound images

Abstract

Speckle noise is an inherent property of medical ultrasound imaging, and it generally tends to reduce the image resolution and contrast, thereby reducing the diagnostic value of this imaging modality. As a result, speckle noise reduction is an important prerequisite, whenever ultrasound imaging is used for tissue characterization. Among the many methods that have been proposed to perform this task, there exists a class of approaches that use a multiplicative model of speckled image formation and take advantage of the logarithmical transformation in order to convert multiplicative speckle noise into additive noise. The common assumption made in a dominant number of such studies is that the samples of the additive noise are mutually uncorrelated and obey a Gaussian distribution. The present study shows conceptually and experimentally that this assumption is oversimplified and unnatural. Moreover, it may lead to inadequate performance of the speckle reduction methods. The study introduces a simple preprocessing procedure, which modifies the acquired radio-frequency images (without affecting the anatomical information they contain), so that the noise in the log-transformation domain becomes very close in its behavior to a white Gaussian noise. As a result, the preprocessing allows filtering methods based on assuming the noise to be white and Gaussian, to perform in nearly optimal conditions. The study evaluates performances of three different, nonlinear filters-wavelet denoising, total variation filtering, and anisotropic diffusion-and demonstrates that, in all these cases, the proposed preprocessing significantly improves the quality of resultant images. Our numerical tests include a series of computer-simulated and in vivo experiments.

Systematic Evaluation on Speckle Suppression Methods in Examination of Ultrasound Breast Images

“…For the same instance, mean and standard deviation were applied to evaluate filtering algorithms [3], and Laplace response was used to evaluate the sharpness degree of filtered images [32]. In that way, a contaminated medical ultrasound image I g = I f · n m + n a [4] can be seen as the homogeneous area which is polluted by additive noise, and the edge of the area becomes vague caused by the degraded function. Therefore, we regard the de-noising and inverse degradation process as: (1) improvement of the smoothness in the homogeneous region; and (2) sharpening the details of the edge region.…”

“…The speckle is known to hamper object recognition [3]. The existence of speckle disrupts clinical diagnosis, especially the computer-aided diagnosis (CAD), not only by reducing the image resolution and contrast [4] and hence causing classification errors of the breast tumor, but also by adding difficulties to subsequent image processing, e.g., tumor region segmentation and recognition. Hence, speckle suppression filtering (to achieve smoothness in the homogeneous region and clearer details in the edge region) is a prerequisite procedure in BUS image processing to promote image quality and tumor segmentation accuracy.…”

“…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 · 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.…”

An Efficient Speckle Noise Reduction Filter in Ultrasound images using Discrete Wavelet Transform based on Adaptive Threshold

“…One of its main shortcomings is the poor quality of images, which are affected by speckle noise. The existence of speckle is unattractive since it disgraces the image quality and affects the task of individual interpretation and diagnosis [5]. Accordingly, the speckle filtering is a central preprocessing step for feature extraction, analysis and recognition from medical imagery measurements.…”

An Effective Approach of Lesion Segmentation Within the Breast Ultrasound Image Based on the Cellular Automata Principle

“…In addition, the quality of ultrasound image is degraded by speckle noise. Although some speckle-removing methods were utilized for reducing noise [19,20], the trade-off between noise suppression and feature preservation is a dilemma. Speckle patterns can reflect the local echogeneity of the underlying scatters, which can be employed by some methods, e.g., MRF-based methods [21], and the details of the images may be damaged after noise removal.…”