Assessment of noise reduction in ultrasound images of common carotid and brachial arteries

Rafati, Mehravar; Arabfard, Masoud; Zadeh, Mehrdad Rafati Rahim; Maghsoudloo, Mazaher

doi:10.1049/iet-cvi.2014.0151

Cited by 19 publications

(8 citation statements)

References 35 publications

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“…A despeckling residual neural network (DRNN) in which a discriminator inserts a loss function and distinguishes the denoised image and the other similar images. 76 The author proves that removing the speckle entirely will lead to the removal of necessary details. The loss function prevents over smoothing leading to better-denoised image.…”

Section: Speckle Denoisingmentioning

confidence: 99%

Carotid artery ultrasound image analysis: A review of the literature

Latha

Dhanalakshmi

Kumar

2020

Proc Inst Mech Eng H

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Stroke is one of the prominent causes of death in the recent days. The existence of susceptible plaque in the carotid artery can be used in ascertaining the possibilities of cardiovascular diseases and long-term disabilities. The imaging modality used for early screening of the disease is B-mode ultrasound image of the person in the artery area. The objective of this article is to give a widespread review of the imaging modes and methods used for studying the carotid artery for identifying stroke, atherosclerosis and related cardiovascular diseases. We encompass the review in methods used for artery wall tracking, intima–media, and lumen segmentation which will help in finding the extent of the disease. Due to the characteristics of the imaging modality used, the images have speckle noise which worsens the image quality. Adaptive homomorphic filtering with wavelet and contourlet transforms, Levy Shrink, gamma distribution were used for image denoising. Learning-based neural network approaches for denoising give better edge preservation. Domain knowledge-based segmentation approaches have proved to provide more accurate intima–media thickness measurements. There is a requirement of useful fully automatic segmentation approaches, 3D, 4D systems, and plaque motion analysis. Taking into consideration the image priors like geometry, imaging physics, intensity and temporal data, image analysis has to be performed. Encouragingly more research has focused on content-specific segmentation and classification techniques. With the evaluation of machine learning algorithms, classifying the image as with or without a fat deposit has gained better accuracy and sensitivity. Machine learning–based approaches like self-organizing map, k-nearest neighborhood and support vector machine achieve promising accuracy and sensitivity in classification. The literature reveals that there is more scope in identifying a patient-specific model in a fully automatic manner.

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Section: Speckle Denoisingmentioning

confidence: 99%

Carotid artery ultrasound image analysis: A review of the literature

Latha

Dhanalakshmi

Kumar

2020

Proc Inst Mech Eng H

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“…Sobel [1], Prewitt [2] and Canny [3] are basic edge detection algorithms used in the literature. There are many studies on images using these popular algorithms [4][5][6][7][8][9][10][11][12][13][14][15][16].…”

Section: Introductionmentioning

confidence: 99%

Comparison of the Edge Detection Methods According to Fake Edge and Area Calculation Performances

Güngör

Polat

2020

International Journal of Automotive Science and Technology

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Determining the borders of the objects in the images and detection of the edges is an important step for image processing applications. In this study, the areas of different objects were calculated using edge detection methods. The edges of real-time images taken from the camera were detected with traditionally used Sobel, Prewitt and Canny filters. In addition, these filters were applied to the images obtained using Basic Global Thresholding (BGT) pre-processing and Mean Thresholding (MT) pre-processing to detect the edges in the image. After eliminating the fake edges in the edge images, the areas of the obtained images were calculated. The methods used in this paper were compared according to the fake edge and the area calculation performances.

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“…In the pursuit of keeping the edges intact, Canny edge detectors are used along with the filtering procedure. SRAD filter combined with a Canny edge detector was introduced which displayed better speckle suppression results [20]. Variational methods based on total variation regularisation play an important role in image denoising since they can preserve discontinuities in recovered images [21].…”

Section: Introductionmentioning

confidence: 99%