Book ChapterDOI
Edge Aware Geometric Filter for Ultrasound Image Enhancement
Deepak Mishra,Santanu Chaudhury,Santanu Chaudhury,Mukul Sarkar,Arvinder S. Soin +4 more
- pp 109-120
TLDR
The proposed filter requires almost no parameter tuning and provides good quality outputs for synthetic as well as real ultrasound images and is compared with the state-of-the-art speckle reducing filters.Abstract:
Despeckling of ultrasound images is essential for subsequent computational analysis. In this paper, an edge aware geometric filter (GF) is proposed for speckle reduction. The behaviour of conventional GF is approximated using commonly used functions like unit step. These approximations help in identifying the natural relationship between GF and other existing spatially adaptive filters. Subsequently, the modifications in GF framework are proposed to take the advantage of edge characteristics. The proposed filter requires almost no parameter tuning and provides good quality outputs for synthetic as well as real ultrasound images. It is compared with the state-of-the-art speckle reducing filters. Improvements of 10.46% and 42% are noticed in mean square error and figure of merit, respectively.read more
Citations
More filters
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.
Proceedings ArticleDOI
Despeckling CNN with Ensembles of Classical Outputs
TL;DR: A convolutional neural network is developed which learns to remove speckle from US images using the outputs of these classical approaches and is able to outperform the state-of-the-art despeckling approaches and produces the outputs even better than the ensembles for certain images.
References
More filters
Journal ArticleDOI
Comparative evaluation of despeckle filtering in ultrasound imaging of the carotid artery
Christos P. Loizou,Constantinos S. Pattichis,C.I. Christodoulou,Robert S. H. Istepanian,Marios Pantziaris,Andrew N. Nicolaides +5 more
TL;DR: A comparative evaluation of despeckle filtering based on texture analysis, image quality evaluation metrics, and visual evaluation by medical experts in the assessment of 440 ultrasound images of the carotid artery bifurcation suggests that the first order statistics filter lsmv, gave the best performance, followed by the geometric filter gf4d, and the homogeneous mask area filter l sminsc.
Journal ArticleDOI
Geometric filter for speckle reduction.
TL;DR: Tests were performed on synthetic aperture radar images which show that the algorithm reduces speckle noise in images favorably with a 3 × 3 median filter.
Journal ArticleDOI
Nonlinear multiscale wavelet diffusion for speckle suppression and edge enhancement in ultrasound images
TL;DR: A novel nonlinear multiscale wavelet diffusion method for ultrasound speckle suppression and edge enhancement designed to utilize the favorable denoising properties of two frequently used techniques: the sparsity and multiresolution properties of the wavelet and the iterative edge enhancement feature of nonlinear diffusion.
Journal ArticleDOI
Ultrasound image enhancement: A review
TL;DR: This paper classified these techniques for ultrasound enhancement into two groups: preprocessing and post-processing, analyzed their benefits and limitations, and presented beliefs about where ultrasound research could be directed to, in order to improve its effectiveness and broaden its applications.
Journal ArticleDOI
Ultrasound Despeckling for Contrast Enhancement
TL;DR: The evaluations of despeckling performance are based upon improvements to contrast enhancement, structural similarity, and segmentation results on a Field II simulated image and actual B-mode cardiac ultrasound images captured in vivo.
Related Papers (5)
An efficient technique for speckle noise reduction in ultrasound images
Meenal Gupta,Amit Garg +1 more