Thomson's multitaper approach combined with coherent plane-wave compounding to reduce speckle in ultrasound imaging

In recent years, ultrasonography is being used for effective diagnosis of various organs such as the heart, kidney, prostate, liver, ovary, uterus, thyroid glands etc. Unfortunately, one of its shortcomings is the low contrast, high noise images which are an inevitable byproduct. This is due to an artifact known as “Speckle” which obscures fine details in an image and may lead to erroneous diagnosis. Hence Speckle Filtering is a prerequisite in ultrasonography, provided that the features of interest for diagnosis are not lost. This paper presents a Hybrid and multistage Filtering approach in order to reduce the Speckle noise and improve the visual quality for better diagnosis. The performance of our approach is compared with the other Speckle reduction Filters on the basis of image quality parameters like Peak Signal to Noise Ratio (PSNR), Effective Number of Looks (ENL), Image Quality Index (IQI) and Mean Structure Similarity Index Map (MSSIM). We could achieve in Multistage approach a better performance with higher value of PSNR (79.915), IQI (0.9497), MSSIM (0.9945) and ENL (0.0984) compared to Hybrid Filter. General Terms Ultrasound Image, Speckle Noise, Linear Filter, Non-linear Filter

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A Novel Approach for Speckle Reduction and Enhancement of Ultrasound Images

Despeckling of medical ultrasound kidney images in the curvelet domain using diffusion filtering and MAP estimation

BACKGROUND Ultrasonic evaluation of intima-media thickness (IMT) is an early marker of assessing the development of atherosclerosis and determining cardiovascular risk. To attain the best possible diagnosis, it is essential that medical images be clear, sharp and without noise and artifacts. OBJECTIVES Comparison of speckle reducing anisotropic diffusion (SRAD), discrete (DTD) and continuum topological derivative (CTD) on B-mode ultrasound images of common carotid and brachial arteries throughout the cardiac cycle. PATIENTS AND METHODS In a cross-sectional design, an examination was performed on forty-two human subjects with a mean age of 44 ± 6 years from April 2013 to June 2013. This study was approved by the ethics committees of Kashan University of Medical Sciences and Beheshti Hospital. An ultrasonic examination of common carotid and brachial arteries of forty-two human subjects was performed. The program was designed in MATLAB software to extract consecutive B-mode images and apply region of interest (ROI) on the IMT of the common carotid and brachial arteries. Then, three different noise reduction filters with the Canny edge detection were used in ROI separately. Finally, the program measured the image quality metrics. RESULTS According to values of eleven different image quality metrics (mentioned in the main text), there was a significant difference between CTD, DTD and SRAD filters with the Canny edge detection status in the common carotid and brachial arteries throughout the cardiac cycle (all P values < 0.001). For example, peak signal to noise ratios (PSNR) using CTD, DTD and SRAD filters were 95.43 ± 0.64, 88.86 ± 0.82 and 73.02 ± 0.20 in common carotid and 96.39 ± 1.25, 92.58 ± 0.11 and 88.27 ± 0.63 in brachial arteries, respectively (both P values < 0.001). CONCLUSIONS By measuring image quality metrics, this study showed that DTD and CTD filters with the Canny edge detection respectively, are better than SRAD filter with the Canny detection for speckle suppression and details preservation in both arteries in the ultrasound images.

A Comparative Study of Three Speckle Reducing Methods for Intima-Media Thickness Ultrasound Images

In ultrasound imaging and video visual perception is hindered by speckle multiplicative noise that degrades the quality. Noise reduction is therefore essential for improving the visual observation quality or as a pre-processing step for further automated analysis, such as image/video segmentation, texture analysis and encoding in ultrasound imaging and video. The goal of the first book (book 1 of 2 books) was to introduce the problem of speckle in ultrasound image and video as well as the theoretical background, algorithmic steps, and the MatlabTM for the following group of despeckle filters: linear despeckle filtering, non-linear despeckle filtering, diffusion despeckle filtering, and wavelet despeckle filtering. The goal of this book (book 2 of 2 books) is to demonstrate the use of a comparative evaluation framework based on these despeckle filters (introduced on book 1) on cardiovascular ultrasound image and video processing and analysis. More specifically, the despeckle filtering evaluation framework is based on texture analysis, image quality evaluation metrics, and visual evaluation by experts. This framework is applied in cardiovascular ultrasound image/video processing on the tasks of segmentation and structural measurements, texture analysis for differentiating between two classes (i.e. normal vs disease) and for efficient encoding for mobile applications. It is shown that despeckle noise reduction improved segmentation and measurement (of tissue structure investigated), increased the texture feature distance between normal and abnormal tissue, improved image/video quality evaluation and perception and produced significantly lower bitrates in video encoding. Furthermore, in order to facilitate further applications we have developed in MATLABTM two different toolboxes that integrate image (IDF) and video (VDF) despeckle filtering, texture analysis, and image and video quality evaluation metrics. The code for these toolsets is open source and these are available to download complementary to the two monographs.

Despeckle Filtering for Ultrasound Imaging and Video, Volume II: Selected Applications, Second Edition

This paper presents a comparative study of among one Multi Scale filter (wavelet), nine single scale spatial adaptive filters (viz. Anistropic Diffusion (PMAD)) that are widely used for speckle noise reduction in biomedical ultrasound B-Scan images. The main objective of this study is to identify the efficient and optimum speckle filter in terms of preserving the edge details of the images with effective denoising. The performance of the filters are best estimated by calculating twenty-one established performance metrics along with execution time in order to determine the effective and optimum-despeckling algorithm for real time implementation. To do this, we have developed a cumulative speckle reduction (CSR) algorithm in the MATLAB environment, which performs all despeckle filtering functions as well as performance metrics calculation in a single trial. In the case of diffusion filter implementation, provision is given to execute the diffusion filter for several trials to identify the best iteration in terms of denoising the speckle and preserving the diagnostic information found in the B-scan images. The algorithm has been experimented with more than 200 digital ultrasound B-scan images of kidney, abdomen, liver and choroids. Based on the visual inspection of the despeckled images and the calculation of the performance metrics, it is found that SRAD and Wavelet despeckling filters are exhibiting fairly well performance over the other standard spatial filters.

D. Nedumaran

Papers

Speckle Filtering of Ultrasound B-Scan Images- A Comparative Study of Single Scale Spatial Adaptive Filters, Multiscale Filter and Diffusion Filters