Despeckling of 3D ultrasound image using tensor low rank approximation

Sagheer, Sameera V. Mohd; George, Sudhish N.; Kurien, Sudeep Koshy

doi:10.1016/j.bspc.2019.101595

Cited by 7 publications

(7 citation statements)

References 34 publications

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“…The availability of the open databases has also enabled research in other areas related to US guided neurosurgery. One example is the work of Sagheer et al ( 44 ) who used the BITE database to validate their US image denoising algorithm. Other examples include a US probe calibration method ( 45 ) where the authors used the BITE database in the validation process, simulation of 2D US from 3D MR ( 46 ) and sorting of DICOM images ( 47 ).…”

Section: Results—impact Of Existing Databases Bite and Resectmentioning

confidence: 99%

The Essential Role of Open Data and Software for the Future of Ultrasound-Based Neuronavigation

2021

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With the recent developments in machine learning and modern graphics processing units (GPUs), there is a marked shift in the way intra-operative ultrasound (iUS) images can be processed and presented during surgery. Real-time processing of images to highlight important anatomical structures combined with in-situ display, has the potential to greatly facilitate the acquisition and interpretation of iUS images when guiding an operation. In order to take full advantage of the recent advances in machine learning, large amounts of high-quality annotated training data are necessary to develop and validate the algorithms. To ensure efficient collection of a sufficient number of patient images and external validity of the models, training data should be collected at several centers by different neurosurgeons, and stored in a standard format directly compatible with the most commonly used machine learning toolkits and libraries. In this paper, we argue that such effort to collect and organize large-scale multi-center datasets should be based on common open source software and databases. We first describe the development of existing open-source ultrasound based neuronavigation systems and how these systems have contributed to enhanced neurosurgical guidance over the last 15 years. We review the impact of the large number of projects worldwide that have benefited from the publicly available datasets “Brain Images of Tumors for Evaluation” (BITE) and “Retrospective evaluation of Cerebral Tumors” (RESECT) that include MR and US data from brain tumor cases. We also describe the need for continuous data collection and how this effort can be organized through the use of a well-adapted and user-friendly open-source software platform that integrates both continually improved guidance and automated data collection functionalities.

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Section: Results—impact Of Existing Databases Bite and Resectmentioning

confidence: 99%

The Essential Role of Open Data and Software for the Future of Ultrasound-Based Neuronavigation

2021

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“…The key aspect that has made the t-product-based t-SVD so instrumental in many applications (see, for example, refs. [18][19][20][21] is the tensor Eckart-Young theorem proven in ref. 10 for real-valued tensors under the t-product.…”

Section: Definition 36mentioning

confidence: 94%

Tensor-tensor algebra for optimal representation and compression of multiway data

Kilmer

Horesh

Avron

et al. 2021

Proc. Natl. Acad. Sci. U.S.A.

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With the advent of machine learning and its overarching pervasiveness it is imperative to devise ways to represent large datasets efficiently while distilling intrinsic features necessary for subsequent analysis. The primary workhorse used in data dimensionality reduction and feature extraction has been the matrix singular value decomposition (SVD), which presupposes that data have been arranged in matrix format. A primary goal in this study is to show that high-dimensional datasets are more compressible when treated as tensors (i.e., multiway arrays) and compressed via tensor-SVDs under the tensor-tensor product constructs and its generalizations. We begin by proving Eckart–Young optimality results for families of tensor-SVDs under two different truncation strategies. Since such optimality properties can be proven in both matrix and tensor-based algebras, a fundamental question arises: Does the tensor construct subsume the matrix construct in terms of representation efficiency? The answer is positive, as proven by showing that a tensor-tensor representation of an equal dimensional spanning space can be superior to its matrix counterpart. We then use these optimality results to investigate how the compressed representation provided by the truncated tensor SVD is related both theoretically and empirically to its two closest tensor-based analogs, the truncated high-order SVD and the truncated tensor-train SVD.

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“…Existing algorithms [32][33][34][37][38][39] are compared with proposed technique to show the efficiency of the current work.…”

Section: Msementioning

confidence: 99%

“…In Figure 11, the SSIM is measured, and compared with the existing algorithms, namely FSTV, 32 t-SVD, 33 and NSS. 34 The PSNR for the proposed work is 18% which is better than NSS, and 14% better than FSTV, and 14% better than t-SVD.…”

Section: State Of the Art Comparisonmentioning

confidence: 99%

Nonsubsampledcontourlet transform with cross‐guided bilateral filter for despeckling of medical ultrasound images

Joel¹,

Rajagopal²

2020

Int J Imaging Syst Tech

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This paper aims to enhance the image quality in ultrasound images. The significant difficulties in the ultrasound image are the presence of speckle noise. Speckle is the granular noise, and this kind of noise produces a lot of challenges during medical diagnosis. Also, these kinds of problems degrade image quality. The proposed work overcomes this kind of issue with nonsubsampled contourlet domain‐based cross‐guided bilateral filtering. Initially, additive speckle noise is introduced with the log‐transformed Rayleigh distribution. This additive speckle noise component is to be decomposed through the non‐subsampled contourlet domain transform. Then the decomposed image is despeckled by the cross‐guided bilateral filter, and the cost function of this filter is minimized through adaptive galactic swarm optimization. The proposed filtering technique despeckles the additive noise in the noisy image. Finally, the decomposed image is reconstructed using the inverse transform of the nonsubsampled decomposition. The implementation of the proposed methodology is carried out in MATLAB simulation platform. Also, the performance of the proposed work is measured by few metrics such as structural similarity index matrix, mean structural similarity index matrix, mean square error, peak signal to noise ratio, Bhattacharyya coefficient, and speckle index. The result of cost function optimization provides less distortion when compared to other techniques.

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Despeckling of 3D ultrasound image using tensor low rank approximation

Cited by 7 publications

References 34 publications

The Essential Role of Open Data and Software for the Future of Ultrasound-Based Neuronavigation

The Essential Role of Open Data and Software for the Future of Ultrasound-Based Neuronavigation

Tensor-tensor algebra for optimal representation and compression of multiway data

Nonsubsampledcontourlet transform with cross‐guided bilateral filter for despeckling of medical ultrasound images

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