Nonlocal linear minimum mean square error methods for denoising MRI

Sudeep, P.V.; Palanisamy, P.; Kesavadas, Chandrasekharan; Rajan, Jeny

doi:10.1016/j.bspc.2015.04.015

Cited by 35 publications

(6 citation statements)

References 44 publications

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“…Three widely used quantitative metrics, including signal to noise ratio (SNR) [ 26 ], peak signal to noise ratio (PSNR) [ 27 ] and root mean squared error (RMSE) [ 28 ], are used to measure the effectiveness of various algorithms along with the visual evaluation. The SNR is defined as where is the original image, is the denoised image, and the size of image is .…”

Section: Methodsmentioning

confidence: 99%

Rayleigh-maximum-likelihood bilateral filter for ultrasound image enhancement

Miao

et al. 2017

BioMed Eng OnLine

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BackgroundUltrasound imaging plays an important role in computer diagnosis since it is non-invasive and cost-effective. However, ultrasound images are inevitably contaminated by noise and speckle during acquisition. Noise and speckle directly impact the physician to interpret the images and decrease the accuracy in clinical diagnosis. Denoising method is an important component to enhance the quality of ultrasound images; however, several limitations discourage the results because current denoising methods can remove noise while ignoring the statistical characteristics of speckle and thus undermining the effectiveness of despeckling, or vice versa. In addition, most existing algorithms do not identify noise, speckle or edge before removing noise or speckle, and thus they reduce noise and speckle while blurring edge details. Therefore, it is a challenging issue for the traditional methods to effectively remove noise and speckle in ultrasound images while preserving edge details.MethodsTo overcome the above-mentioned limitations, a novel method, called Rayleigh-maximum-likelihood switching bilateral filter (RSBF) is proposed to enhance ultrasound images by two steps: noise, speckle and edge detection followed by filtering. Firstly, a sorted quadrant median vector scheme is utilized to calculate the reference median in a filtering window in comparison with the central pixel to classify the target pixel as noise, speckle or noise-free. Subsequently, the noise is removed by a bilateral filter and the speckle is suppressed by a Rayleigh-maximum-likelihood filter while the noise-free pixels are kept unchanged. To quantitatively evaluate the performance of the proposed method, synthetic ultrasound images contaminated by speckle are simulated by using the speckle model that is subjected to Rayleigh distribution. Thereafter, the corrupted synthetic images are generated by the original image multiplied with the Rayleigh distributed speckle of various signal to noise ratio (SNR) levels and added with Gaussian distributed noise. Meanwhile clinical breast ultrasound images are used to visually evaluate the effectiveness of the method. To examine the performance, comparison tests between the proposed RSBF and six state-of-the-art methods for ultrasound speckle removal are performed on simulated ultrasound images with various noise and speckle levels.ResultsThe results of the proposed RSBF are satisfying since the Gaussian noise and the Rayleigh speckle are greatly suppressed. The proposed method can improve the SNRs of the enhanced images to nearly 15 and 13 dB compared with images corrupted by speckle as well as images contaminated by speckle and noise under various SNR levels, respectively. The RSBF is effective in enhancing edge while smoothing the speckle and noise in clinical ultrasound images. In the comparison experiments, the proposed method demonstrates its superiority in accuracy and robustness for denoising and edge preserving under various levels of noise and speckle in terms of visual quality as well as numeric metr...

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Section: Methodsmentioning

confidence: 99%

Rayleigh-maximum-likelihood bilateral filter for ultrasound image enhancement

Miao

et al. 2017

BioMed Eng OnLine

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“…S. A. Akar used a bilateral filter with a genetic algorithm (BF-GA) for MR image denoising [39]. Sudeep et al [40] used the linear minimum mean square error method (LMMSE) for principal component analysis (PCA) to remove noise from an image. Therefore, motivated according to the research contributions provided by researchers, this paper explored bio-inspired optimization algorithms to provide edge-preserved denoising for medical images as a feasible solution.…”

Section: Introductionmentioning

confidence: 99%

A Novel Guided Box Filter Based on Hybrid Optimization for Medical Image Denoising

2023

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Medical image denoising is a crucial pre-processing task in the medical field to ensure accurate analysis of anomalies or sicknesses in the human body. Digital filters are popular for reducing undesired noise as they provide reliability, high accuracy, and reduced sensitivity to component tolerances compared to analog filters. However, conventional digital filter design approaches lack efficiency in achieving global optimization robustness. To overcome these incapabilities, this paper adopted bio-inspired optimization algorithms to offer viable digital filter designing tools because of their simple implementation and requirement of a few parameters to control their convergence. This research article explores a hybrid strategy that combines a novel guided decimation box filter (GDBF) with a hybrid cuckoo particle swarm optimization (HCPSO) algorithm to design a denoising filter for medical images. It is the first time a decimation box filter has been used for denoising, leading to novelty. The HCPSO algorithm is applied to obtain the filter parameters optimally. Medical images mostly suffer from four types of noises. The performance of the proposed filter is analyzed for these types of noise. To highlight the importance of parameter selection, the results of the proposed method are compared with other recently utilized bio-inspired genetic algorithms, such as PSO (particle swarm optimization), CS (cuckoo search), and FF (firefly). The superiority (potency) of the proposed method has been established by calculating the improvement in quality parameters such as the peak signal-to-noise ratio (PSNR), structure similarity index (SSIM), and feature similarity index (FSIM). The proposed filter achieved the highest PSNR (~35.7 dB), SSIM (~0.95), and FSIM (~0.92) and proved its numerical and visual quality efficacy over state-of-the-art models.

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“…It is necessary to identify and detect these types of noises to improve human body diagnostic method. In [13] a two-step algorithm for removal of Rician noise in MR images is proposed. Four types of filters are proposed and for all of them non-local linear minimum mean square error (LMMSE) estimation is used.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Real-Time Removal of Impulse Noise in Medical Images

et al. 2018

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Noise is an important factor that degrades the quality of medical images. Impulse noise is a common noise, which is caused by malfunctioning of sensor elements or errors in the transmission of images. In medical images due to presence of white foreground and black background, many pixels have intensities similar to impulse noise and distinction between noisy and regular pixels is difficult. In software techniques, the accuracy of the impulse noise removal is more important than the algorithm's complexity. But in development of hardware techniques having a low complexity algorithm with an acceptable accuracy is essential. In this paper a low complexity de-noising method is proposed that removes the noise by local analysis of the image blocks. In this way, noisy pixels are distinguished from non-noisy pixels. All steps are designed to have low hardware complexity. Simulation results show that in the case of magnetic resonance images, the proposed method removes impulse noise with an acceptable accuracy.

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Nonlocal linear minimum mean square error methods for denoising MRI

Cited by 35 publications

References 44 publications

Rayleigh-maximum-likelihood bilateral filter for ultrasound image enhancement

Rayleigh-maximum-likelihood bilateral filter for ultrasound image enhancement

A Novel Guided Box Filter Based on Hybrid Optimization for Medical Image Denoising

Adaptive Real-Time Removal of Impulse Noise in Medical Images

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