Journal ArticleDOI
Edge Probability and Pixel Relativity-Based Speckle Reducing Anisotropic Diffusion
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TLDR
In the subjective evaluation, performed by the expert radiologists, the proposed filter’s outputs are preferred for the improved contrast and sharpness of the object boundaries and the proposed filtering framework is suitable to reduce the unwanted speckle and improve the quality of the ultrasound images.Abstract:
Anisotropic diffusion filters are one of the best choices for speckle reduction in the ultrasound images. These filters control the diffusion flux flow using local image statistics and provide the desired speckle suppression. However, inefficient use of edge characteristics results in either oversmooth image or an image containing misinterpreted spurious edges. As a result, the diagnostic quality of the images becomes a concern. To alleviate such problems, a novel anisotropic diffusion-based speckle reducing filter is proposed in this paper. A probability density function of the edges along with pixel relativity information is used to control the diffusion flux flow. The probability density function helps in removing the spurious edges and the pixel relativity reduces the oversmoothing effects. Furthermore, the filtering is performed in superpixel domain to reduce the execution time, wherein a minimum of 15% of the total number of image pixels can be used. For performance evaluation, 31 frames of three synthetic images and 40 real ultrasound images are used. In most of the experiments, the proposed filter shows a better performance as compared to the state-of-the-art filters in terms of the speckle region’s signal-to-noise ratio and mean square error. It also shows a comparative performance for figure of merit and structural similarity measure index. Furthermore, in the subjective evaluation, performed by the expert radiologists, the proposed filter’s outputs are preferred for the improved contrast and sharpness of the object boundaries. Hence, the proposed filtering framework is suitable to reduce the unwanted speckle and improve the quality of the ultrasound images.read more
Citations
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Journal ArticleDOI
Advances in Data Preprocessing for Biomedical Data Fusion: An Overview of the Methods, Challenges, and Prospects
Shuihua Wang,M. Emre Celebi,Yudong Zhang,Xiang Yu,Siyuan Lu,Xujing Yao,Qinghua Zhou,Martínez-García Miguel,Yingli Tian,Juan Manuel Górriz,Ivan Tyukin +10 more
TL;DR: An overview of the advances in data preprocessing in biomedical data fusion is provided in this article, along with insights stemming from new developments in the field, and an overview of new techniques for data fusion methods are discussed.
Journal ArticleDOI
Ultrasound Image Enhancement Using Structure Oriented Adversarial Network
TL;DR: Experimental evaluations show that the proposed DRNN outperforms the state-of-the-art despeckling approaches in terms of the structural similarity index measure, peak signal to noise ratio, edge preservation index, and speckle region's signal-to- noise ratio.
Journal ArticleDOI
Carotid artery ultrasound image analysis: A review of the literature:
TL;DR: 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.
Journal ArticleDOI
Phase Asymmetry Ultrasound Despeckling With Fractional Anisotropic Diffusion and Total Variation
TL;DR: A new fractional TV framework is proposed to not only achieve the best despeckling performance with ramp edge preservation but also reduce the staircase effect produced by integral-order filters.
Journal ArticleDOI
Anisotropic Diffusion Based Multiplicative Speckle Noise Removal
TL;DR: An anisotropic diffusion model based on image statistics, including information on the gradient of the image, gray levels, and noise standard deviation, is proposed and can effectively remove speckle noise and retain minute details of the images for the real ultrasound and RGB color images.
References
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Proceedings ArticleDOI
Histograms of oriented gradients for human detection
Navneet Dalal,Bill Triggs +1 more
TL;DR: It is shown experimentally that grids of histograms of oriented gradient (HOG) descriptors significantly outperform existing feature sets for human detection, and the influence of each stage of the computation on performance is studied.
Journal ArticleDOI
Scale-space and edge detection using anisotropic diffusion
Pietro Perona,Jitendra Malik +1 more
TL;DR: A new definition of scale-space is suggested, and a class of algorithms used to realize a diffusion process is introduced, chosen to vary spatially in such a way as to encourage intra Region smoothing rather than interregion smoothing.
Proceedings ArticleDOI
Bilateral filtering for gray and color images
Carlo Tomasi,Roberto Manduchi +1 more
TL;DR: In contrast with filters that operate on the three bands of a color image separately, a bilateral filter can enforce the perceptual metric underlying the CIE-Lab color space, and smooth colors and preserve edges in a way that is tuned to human perception.
Journal ArticleDOI
SLIC Superpixels Compared to State-of-the-Art Superpixel Methods
TL;DR: A new superpixel algorithm is introduced, simple linear iterative clustering (SLIC), which adapts a k-means clustering approach to efficiently generate superpixels and is faster and more memory efficient, improves segmentation performance, and is straightforward to extend to supervoxel generation.
Proceedings ArticleDOI
A non-local algorithm for image denoising
TL;DR: A new measure, the method noise, is proposed, to evaluate and compare the performance of digital image denoising methods, and a new algorithm, the nonlocal means (NL-means), based on a nonlocal averaging of all pixels in the image is proposed.