Speckle reduction in ultrasonic images through a maximum likelihood based adaptive filter

Shankar, P.M.

doi:10.1088/0031-9155/51/21/014

Cited by 9 publications

(3 citation statements)

References 33 publications

(38 reference statements)

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“…Belohlavek et al [5] uses the eight hull algorithm with a geometric filter [10]. Recent techniques are based on region growing [9], adaptive filtering [67], compression techniques [26] and anisotropic diffusion filters [38].…”

Section: Data Enhancementmentioning

confidence: 99%

The Ultrasound Visualization Pipeline

Birkeland

Šoltészová

Hönigmann

et al. 2014

Mathematics and Visualization

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Note: Also to appear in the Dagstuhl 2012 SciVis book by Springer. Please cite this paper with its arXiv citation information. Abstract Ultrasound is one of the most frequently used imaging modality in medicine. The high spatial resolution, its interactive nature and non-invasiveness makes it the first choice in many examinations. Image interpretation is one of ultrasound's main challenges. Much training is required to obtain a confident skill level in ultrasound-based diagnostics. State-of-the-art graphics techniques is needed to provide meaningful visualizations of ultrasound in real-time. In this paper we present the process-pipeline for ultrasound visualization, including an overview of the tasks performed in the specific steps. To provide an insight into the trends of ultrasound visualization research, we have selected a set of significant publications and divided them into a technique-based taxonomy covering the topics pre-processing, segmentation, registration, rendering and augmented reality. For the different technique types we discuss the difference between ultrasound-based techniques and techniques for other modalities.

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Section: Data Enhancementmentioning

confidence: 99%

The Ultrasound Visualization Pipeline

Birkeland

Šoltészová

Hönigmann

et al. 2014

Mathematics and Visualization

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“…Also, there are many algorithms depend on statistical measures are used to reduce the noise such as: The median filter is effective in eliminating noise, where it replaces the middle pixel in the window with the median value of its neighbors [7]. Also, maximum-likelihood filter and Bayesian denoising method is used to remove the noise models and adopt the robust parametric estimation approaches [8,9]. However, these filters suffer from some drawbacks such as: they can remove the relevant feature from the image and also the blurring problem.…”

Section: Introductionmentioning

confidence: 99%

Medical Image Denoising based on Log-Gabor Wavelet Dictionary and K-SVD Algorithm

Farouk¹,

El-Sayed²,

Aly³

2016

IJCA

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Medical image denoising is the main step in medical diagnosis, which removes the noise without affecting relevant features of the image. There are many algorithms that can be used to reduce the noise such as: threshold and the sparse representation. The K-SVD is one of the most popular sparse representation algorithms, which is depend on Orthogonal Matching Pursuit (OMP) and Discrete Cosine Transform (DCT) dictionary. In this paper, an algorithm for image denoising was designed to develop K-SVD by using Regularized Orthogonal Matching Pursuit (ROMP) over log Gabor wavelet adaptive dictionary. To evaluate the performance of the proposed techniques, the results were compared with the results of DCT and Gabor wavelet dictionary. The numerical results show that the performance of our algorithm is more efficient in medical image denoising. KeywordsSparse representation (SR), K-SVD, log-Gabor wavelet dictionary, regularized orthogonal matching pursuit and orthogonal matching pursuit.

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“…Anisotropic diffusion-based methods control the process of anisotropic diffusion based on statistical characteristics of noise [7], [8]. The parametric estimation methods like the maximum-likelihood filter and Bayesian denoising method establish noise models and adopt the robust parametric estimation approaches [9], [10]. The wavelet-based denoising algorithms based on multi-scale decompositions of the noisy images, apply soft thresholds to wavelet coefficients of different scales to eliminate the noise [11], [12].…”

Section: Introductionmentioning

confidence: 99%

A Global Approach for Medical Image Denoising via Sparse Representation

Guo¹,

Chai²,

Wang³

2015

IJBBB

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In this paper, a novel global noise reduction approach based on the sparse representation and nonlocal means algorithm is proposed to enhance the image qualities of various medical imaging modalities, including ultrasound images and magnetic resonance images. By using an overcomplete dictionary, a medical image is decomposed into a sparsest coefficients matrix populated primarily with zeros. A nonlocal means algorithm is developed to deal with these sparse coefficients to exploit the repetitive characters of structures in the whole image, realizing a "truly" global denoising. With synthetic and clinical data of ultrasound images and magnetic resonance images, this approach has been compared with other five state-of-the-art denoising methods. The experiments quantitative results demonstrate the effectiveness of our approach, especially superior in reducing the noise while well preserving the tissue details. It is concluded that our proposed approach is capable of enhancing image quality in both ultrasound and magnetic resonance images. It has a broad field of applications and will increase the diagnostic potential of the medical images.

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Speckle reduction in ultrasonic images through a maximum likelihood based adaptive filter

Cited by 9 publications

References 33 publications

The Ultrasound Visualization Pipeline

The Ultrasound Visualization Pipeline

Medical Image Denoising based on Log-Gabor Wavelet Dictionary and K-SVD Algorithm

A Global Approach for Medical Image Denoising via Sparse Representation

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