Ultrasound image despeckling using low rank matrix approximation approach

Sagheer, Sameera V. Mohd; George, Sudhish N.

doi:10.1016/j.bspc.2017.06.011

Cited by 35 publications

(8 citation statements)

References 32 publications

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“…With the development of society, in recent years, the incidence of breast diseases in my country has been increasing, among which malignant breast tumors are more serious [ 1 , 2 ]. This disease has a major impact on women's health and gradually develops towards younger age [ 3 , 4 ].…”

Section: Introductionmentioning

confidence: 99%

The Application of Ultrasound Image in Cancer Diagnosis

Wang

Yang

2021

Journal of Healthcare Engineering

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In order to solve the problem of low accuracy, high cost, and difficult detection of traditional algorithms, a new algorithm based on ultrasound imaging is proposed in this paper. The algorithm is based on fuzzy clustering to diagnose the disease and effectively improve the accuracy of the algorithm. By testing the actual scene, it effectively proves the effectiveness and accuracy of the algorithm, which can find and treat diseases in time and can be widely used and popularized in clinical research in China.

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Section: Introductionmentioning

confidence: 99%

The Application of Ultrasound Image in Cancer Diagnosis

Wang

Yang

2021

Journal of Healthcare Engineering

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“…Full-reference metrics. To objectively reflect the despeckling performance of different filters, the peak signal-to-noise ratio (PSNR), 34 mean structure similarity (MSSIM), 35 and edge preservation index (EPI) 36 are adopted in the synthetic image experiment.…”

Section: Performance Evaluation Metricsmentioning

confidence: 99%

“…Thus, the performance of despeckling filters is difficult to be evaluated by using subjective evaluation metric. 34 Many conventional metrics can be used to objectively evaluate the quality of despeckling algorithm such as PSNR, MSSIM, and EPI. However, these metrics require a noise-free image as a standard image, and the noise free ultrasound images do not exist.…”

Section: Performance Evaluation Metricsmentioning

confidence: 99%

Speckle Reduction in Ultrasound Images of the Common Carotid Artery Based on Integer and Fractional-Order Total Variation

Wang

Zhang

2022

Ultrason Imaging

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Designing a technique with higher speckle noise suppressing capability, better edge preserving performance, and lower time complexity is a research objective for the common carotid artery (CCA) ultrasound despeckling. Total variation based techniques have been widely used in the image denoising and have good performance in preserving the edges in the images. However, the total variation based filters can produce the staircase artifacts. To address this issue, second-order total variation based techniques have been proposed for the image denoising. However, the previous study has been proved that the fractional differential model has better performance in reducing the speckles in ultrasound despeckling compared with the second-order model. Thus, to improve the performance of ultrasound despeckling and edge preserving, a novel despeckling model based on integer and fractional-order total variation (IFOTV) is proposed for CCA ultrasound images. Moreover, the minimization problems in our despeckling model are solved by the alternating direction method of multiplier (ADMM). In results with synthetic images, the edge preservation index (EPI) values of proposed method are 0.9524, 0.8797, and 0.7351 as well as 0.9137, 0.8253, and 0.6847 under three different levels of noise, which are the highest among four advanced methods. In results with simulated CCA ultrasound images, the speckle suppression and mean preservation indices of proposed method are 0.5596, 0.6571, and 0.8106 under three different levels of noise, which are the best among four advanced methods. In results with clinical images, the average absolute error of intima-media thickness measurements of proposed method is 0.0660 ± 0.0679 (mean ± std in mm), which is the lowest among four advanced methods. In conclusion, the IFOTV method has improved performance in suppressing the speckle noise and preserving the edge, and is thus a potential alternative to the current filters for the CCA ultrasound despeckling.

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“…Twenty convolutional layers and a regression layer are used to design the denoised CNN (DnCNN) filtre in [53]. Similarly, some of the recently proposed state-of-the-art filtering approaches such as speckle-reducing bilateral filtre (SRBF) [54], non-local means-based speckle filtre (NLMSF) [55] and low-rank matrix-approximation despeckling filtre (LMDF) [56] are also considered to be effective for denoising purpose. The proposed methodology is discussed in the next section.…”

Section: Related Workmentioning

confidence: 99%

Adaptive comprehensive particle swarm optimisation‐based functional‐link neural network filtre model for denoising ultrasound images

Kumar

Mishra

Joseph

et al. 2021

IET Image Processing

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Multiplicative speckle is a dominant type of noise that spoils the inherent features of the medical ultrasound (US) images. Apart from the speckle, impulse and Gaussian noises also appear in the US image due to the error encountered during the data transmission and transition of switching circuits and sensors. The noise not only deteriorates the visual quality of the US but also creates complications in the diagnosis. In this study, an adaptive comprehensive particle swarm optimisation‐based functional‐link neural network (ACPSO‐FLNN) filtre has been proposed and implemented in filtering noisy US images in different noise conditions. The proposed filtre is compared with some state‐of‐the‐art filtering techniques. Quantitative and qualitative measures such as training time, time complexity, convergence rate, and statistical test are included to study the performance of the proposed filtre. Furthermore, sensitivity, computational complexity, and order of the proposed filtre are also investigated. Friedman's test with 50 images is performed for statistical validation. The lower rank, that is, 6 and critical value of 21 × 10–4 of the proposed ACPSO‐FLNN filtre validates its dominance over other filtres.

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Ultrasound image despeckling using low rank matrix approximation approach

Cited by 35 publications

References 32 publications

The Application of Ultrasound Image in Cancer Diagnosis

The Application of Ultrasound Image in Cancer Diagnosis

Speckle Reduction in Ultrasound Images of the Common Carotid Artery Based on Integer and Fractional-Order Total Variation

Adaptive comprehensive particle swarm optimisation‐based functional‐link neural network filtre model for denoising ultrasound images

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