scholarly journals A New Ultrasound Speckle Reduction Algorithm Based on Superpixel Segmentation and Detail Compensation

2019 ◽  
Vol 9 (8) ◽  
pp. 1693 ◽  
Author(s):  
Yang Chen ◽  
Ming Zhang ◽  
Hong-Mei Yan ◽  
Yong-Jie Li ◽  
Kai-Fu Yang
Keyword(s):  
High Frequency ◽  
Speckle Noise ◽  
Speckle Reduction ◽  
Frequency Noise ◽  
Ultrasound Images ◽  
Bilateral Filtering ◽  
Superpixel Segmentation ◽  
Local Structures ◽  
Local Correlations ◽  
Spatial Compensation

Speckle is a kind of noise commonly found in ultrasound images (UIs). Although traditional local operation-based methods, such as bilateral filtering, perform well in de-noising normal natural images with suitable parameters, these methods may break local correlations and, hence, their performance will be highly degraded when applied to UIs with high levels of speckle noise. In this work, we propose a new method, based on superpixel segmentation and detail compensation, to reduce UI speckle noise. In particular, considering that superpixel segmentation has the advantage of adhering accurately to the boundaries of objects or local structures, we propose a superpixel version of bilateral filtering to better protect the local structure during de-noising. Additionally, a human visual system (HVS)-inspired strategy for spatial compensation is introduced, in order to recover sophisticated edges as much as possible while weakening the high-frequency noise. Experiments on synthetic images and real UIs of different organs show that, compared to other methods, the proposed strategy can reduce ultrasound speckle noise more effectively.

Speckle Suppressing Improved Oriented Speckle Reducing Anisotropic Diffusion (IOSRAD) Filter for Medical Ultrasound Images

2014 ◽  
Vol 626 ◽  
pp. 106-110
Author(s):  
Jemila R. Rose ◽  
S. Allwin
Keyword(s):  
Anisotropic Diffusion ◽  
Imaging System ◽  
Speckle Noise ◽  
Quality Metrics ◽  
Speckle Reduction ◽  
Ultrasound Images ◽  
Optimal Method ◽  
Anisotropic Diffusion Filter ◽  
Image Edges ◽  
Speed Accuracy

— Ultrasound imaging is the most commonly used imaging system in medical field. Main problem related to this imaging technique is introduction of speckle noise, thus making the image unclear. The success of ultrasonic examination depends on the image quality which is usually retarded due to speckle noise. There have been several techniques for effective suppression of speckle noise present in ultrasound images. The filtering techniques considered include anisotropic diffusion, wavelet de-noising, and local statistics. Comparison of the filters is based on their application of objective quality metrics, which quantifies the preservation of image edges, overall image distortion, and improvement in image contrast. The computational analysis quantifies the number of operations required for each speckle reduction method. A speed-accuracy analysis of various methods for anisotropic diffusion is included. It is concluded that the optimal method is the OSRAD (Oriented Speckle Reducing Anisotropic Diffusion) filter. The proposed approach technique deals with an improved OSRAD filter which gives an efficient result other than the previous filters by analysing the quality metrics.

Speckle Noise Diffusion in Knee Articular Cartilage Ultrasound Images

2020 ◽  
Vol 16 (6) ◽  
pp. 739-751
Author(s):  
Muhammad Ali Shoaib ◽  
Md Belayet Hossain ◽  
Yan Chai Hum ◽  
Joon Huang Chuah ◽  
Maheza Irna Mohd Salim ◽  
...  
Keyword(s):  
Articular Cartilage ◽  
Contrast Ratio ◽  
Speckle Noise ◽  
Absolute Error ◽  
Cartilage Injury ◽  
Speckle Reduction ◽  
Ultrasound Images ◽  
Low Contrast ◽  
Reduction Techniques ◽  
Articular Cartilage Injury

Background: Ultrasound (US) imaging can be a convenient and reliable substitute for magnetic resonance imaging in the investigation or screening of articular cartilage injury. However, US images suffer from two main impediments, i.e., low contrast ratio and presence of speckle noise. Aims: A variation of anisotropic diffusion is proposed that can reduce speckle noise without compromising the image quality of the edges and other important details. Methods: For this technique, four gradient thresholds were adopted instead of one. A new diffusivity function that preserves the edge of the resultant image is also proposed. To automatically terminate the iterative procedures, the Mean Absolute Error as its stopping criterion was implemented. Results: Numerical results obtained by simulations unanimously indicate that the proposed method outperforms conventional speckle reduction techniques. Nevertheless, this preliminary study has been conducted based on a small number of asymptomatic subjects. Conclusion: Future work must investigate the feasibility of this method in a large cohort and its clinical validity through testing subjects with a symptomatic cartilage injury.

Framelet transform and fuzzy clustering-based intelligent technique for speckle noise removal in ultrasound images

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Praveen Kumar Lendale ◽  
N.M. Nandhitha
Keyword(s):  
Performance Indicators ◽  
Medical Images ◽  
Speckle Noise ◽  
Noise Removal ◽  
Speckle Reduction ◽  
Ultrasound Images ◽  
Clustering Methods ◽  
Edge Preservation ◽  
Content Type ◽  
Intelligent Technique

PurposeSpeckle noise removal in ultrasound images is one of the important tasks in biomedical-imaging applications. Many filtering -based despeckling methods are discussed in many existing works. Two-dimensional (2-D) transforms are also used enormously for the reduction of speckle noise in ultrasound medical images. In recent years, many soft computing-based intelligent techniques have been applied to noise removal and segmentation techniques. However, there is a requirement to improve the accuracy of despeckling using hybrid approaches.Design/methodology/approachThe work focuses on double-bank anatomy with framelet transform combined with Gaussian filter (GF) and also consists of a fuzzy kind of clustering approach for despeckling ultrasound medical images. The presented transform efficiently rejects the speckle noise based on the gray scale relative thresholding where the directional filter group (DFB) preserves the edge information.FindingsThe proposed approach is evaluated by different performance indicators such as the mean square error (MSE), peak signal to noise ratio (PSNR) speckle suppression index (SSI), mean structural similarity and the edge preservation index (EPI) accordingly. It is found that the proposed methodology is superior in terms of all the above performance indicators.Originality/valueFuzzy kind clustering methods have been proved to be better than the conventional threshold methods for noise dismissal. The algorithm gives a reconcilable development as compared to other modern speckle reduction procedures, as it preserves the geometric features even after the noise dismissal.

The Maltese Cross Processor: Speckle Reduction for Circular Transducers

1988 ◽  
Vol 10 (3) ◽  
pp. 153-170 ◽  
Author(s):  
S.W. Smith ◽  
O.T. von Ramm
Keyword(s):  
Signal Processing ◽  
Dynamic Range ◽  
Imaging System ◽  
Speckle Noise ◽  
Processing Technique ◽  
Speckle Reduction ◽  
Ultrasound Images ◽  
Signal Processing Technique ◽  
Aperture Diameter ◽  
Parallel Signal Processing

A new online signal processing technique is described to reduce speckle noise in ultrasound images. In the imaging system, a focused piston transducer is divided into thirty-two sectors. In the receive mode, parallel signal processing arranges the sectors into eight maltese crosses. The rf signals of the perpendicular arms of each cross are multiplied in a phase sensitive process. The orthogonal receive mode multiplication is designed to reduce side lobes resulting from the sector shapes while maintaining lateral resolution through the use of the full aperture diameter. The signals from the crosses are then combined via postdetection summation. Six of the eight crosses perform successfully. The six maltese crosses show decorrelated signals equivalent to four independent samples of the speckle noise which decreases noise contrast by a factor of two with no measureable loss of spatial resolution. Post summation compression is included to retain the conventional signal dynamic range. Parallel signal processing maintains the normal image line rate. Images of tissue-mimicking phantoms including speckle targets show improved detectability of simulated lesions.

scholarly journals Using the method of renormalization with limitation for medical ultrasound image processing.

2020 ◽  
Vol 2020 (10) ◽  
Author(s):  
A.V. Kokoshkin ◽  
Keyword(s):  
Image Processing ◽  
Ultrasound Image ◽  
Speckle Noise ◽  
Ultrasound Images ◽  
Medical Ultrasound ◽  
Bilateral Filtering ◽  
Ultrasound Image Processing ◽  
Images Processing

This article proposes the application of the technique of combining the modernized method of renormalization with limitation with subsequent bilateral filtering to improve the quality of medical ultrasound images. Processing according to this algorithm increases the overall contrast of the image, smoothes out speckle noise and makes it possible to cope well with determining the localization of significant objects. The presented results indicate a significant improvement in the quality of ultrasound images, which can serve as an auxiliary tool for medical workers when clarifying the diagnosis.

scholarly journals Novel image enhancement approaches for despeckling in ultrasound images for fibroid detection in human uterus

2021 ◽  
Vol 11 (1) ◽  
pp. 399-410
Author(s):  
Kaitheri Thacharedath Dilna ◽  
Duraisamy Jude Hemanth
Keyword(s):  
Image Processing ◽  
Signal To Noise Ratio ◽  
Ultrasound Image ◽  
Speckle Noise ◽  
Speckle Reduction ◽  
Ultrasound Images ◽  
Signal To Noise ◽  
Statistical Measures ◽  
Noise Ratio ◽  
Scanned Images

Abstract Ultrasonography is an extensively used medical imaging technique for multiple reasons. It works on the basic theory of echoes from the tissues under consideration. However, the occurrence of signal dependent noise such as speckle destroys utility of ultrasound images. Speckle noise is subject to the composition of image tissue and parameters of image. It reduces the effectiveness of many image processing steps and decreases human perception of fine details form ultrasound images. In many medical image processing methods, despeckling is used as the preprocessing step before segmentation and feature extraction. Many speckle reduction filters are proposed but while combining many techniques some speckle diagnostic information should be preserved. Removal of speckle noise from ultrasound image by preserving edges and added features is a great challenging task in ultrasound image restoration. This paper aims at a comprehensive description and comparison of reduction of speckle noise of ultrasound fibroid image. Many filters are applied on ultrasound scanned images and the performance is marked in terms of some statistical measures. Even though several despeckling filters are there for speckle reduction, all are not good for ultrasound scanned images. A comparison of quality measures such as mean square error, peak signal-to-noise ratio, and signal-to-noise ratio is done in ultrasound images in despeckling.

De-Speckling of Ultrasound Images Using Local Statistics-Based Trilateral Filter

2019 ◽  
Vol 28 (09) ◽  
pp. 1950150 ◽  
Author(s):  
S. Jayanthi Sree ◽  
C. Vasanthanayaki
Keyword(s):  
Adaptive Filters ◽  
Region Of Interest ◽  
Speckle Noise ◽  
Speckle Reduction ◽  
Destructive Interference ◽  
Ultrasound Images ◽  
Edge Preserving ◽  
Local Statistics ◽  
Trilateral Filter ◽  
Anisotropic Diffusion Filters

Speckle noise in ultrasound images is a major hindrance for the automation of segmentation, detection, classification and measurements of region of interest, to assist clinician for diagnosing pathologies. Speckle noise occurs due to constructive and destructive interference of the echo signals reflected from the target and has a granular appearance. Various techniques have been devised for speckle reduction. Most of these techniques are based on adaptive filters, wavelet transform and anisotropic diffusion filters. In this paper, a new speckle reduction technique based on the trilateral filter and local statistics of the image has been developed. The local speckle content of the image influences the trilateral filtering. The trilateral filter is a robust edge preserving filter which considers the similarity of neighboring regions in terms of adjacency, intensity and edge details. Hence, the new method preserves the finer details of the ultrasound images in the process of filtering speckle noise. The proposed technique is validated using synthetic, simulated and real-time clinical ultrasound images. Comparison of the proposed technique with the existing speckle removal algorithms in terms of quality metrics such as MSE, PSNR, UQI, SSI, FoM has been made and best results are obtained for the proposed technique.

Modeling of Log-Transformed Speckle Noise in the Contourlet Transform Domain for Medical Ultrasound Images

2014 ◽  
Vol 24 (02) ◽  
pp. 1540004 ◽  
Author(s):  
Shahriar Mahmud Kabir ◽  
Mohammed Imamul Hassan Bhuiyan
Keyword(s):  
Estimation Method ◽  
Speckle Noise ◽  
Diagnostic Value ◽  
Contourlet Transform ◽  
Speckle Reduction ◽  
Ultrasound Images ◽  
Medical Ultrasound ◽  
Transform Domain ◽  
Processing Step ◽  
Bessel K Form

Speckle noise in medical ultrasound (US) degrades the image quality and reduces its diagnostic value. Reduction of speckle noise is an important pre-processing step for the analysis and processing of medical ultrasound images. Knowledge of the statistics of the log-transformed speckle especially in the multi-resolution transform domain is important for developing effective homomorphic despeckling techniques, the most popular approach of speckle reduction from ultrasound images. In this paper, the bessel K-form (BKF) probability density function (pdf) is proposed as a highly suitable prior for modeling the log-transformed speckle noise in the well-known contourlet transform domain. A maximum likelihood based method is introduced for estimating the parameters of the BKF pdf. The effectiveness of the proposed estimation method is demonstrated using Monte Carlo simulations. The appropriateness of the BKF pdf in modeling the speckle is first studied extensively for simulated noise of different levels in the contourlet transform domain. Next, the suitability of BKF model is investigated for the case of real US images that include neonatal brain and breast tumors. It is shown that, in general the BKF prior can model the statistics of the contourlet transform coefficients corresponding to the log-transformed speckle better than the traditionally used Gaussian, normal inverse Gaussian and generalized Nakagami pdfs.

Advances in Ultrasound Despeckling

2019 ◽  
pp. 311-335
Author(s):  
Sudeep P. V. ◽  
Palanisamy P. ◽  
Jeny Rajan
Keyword(s):  
Speckle Pattern ◽  
Speckle Noise ◽  
Image Understanding ◽  
Performance Comparison ◽  
Low Noise ◽  
Speckle Reduction ◽  
Computer Assisted ◽  
Ultrasound Images ◽  
Medical Ultrasound ◽  
Assisted Diagnosis

The B-mode ultrasound images are corrupted due to the presence of speckle noise. Hence, the speckle removal in the ultrasound images is essential for proper clinical examination and quantitative assessments. The speckle pattern varies with several imaging parameters as well as the anatomical structure in the image. It is hard to avoid speckle by performing averaging and low noise system designs. An excessive speckle reduction diminishes the visibility of small anatomical structures and thereby makes the image understanding complicated. This chapter is intended to encapsulate various techniques for reducing speckle in medical ultrasound images and improving the image quality for visual inspection and/or computer-assisted diagnosis of ultrasound images. In addition, the chapter surveys the papers published between 2015 and 2018 to highlight the latest trends in the despeckling of ultrasound images. The chapter also presents the performance comparison of a few popular algorithms to despeckle medical ultrasound images.

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