A Survey on Despeckling Filters for Speckle Noise Removal in Ultrasound Images

Karthikeyan, Sivashanmugam; Manikandan, T.; Nandalal, V.; Iqbal, J. L. Mazher; Babu, J. Jai Jaganath

doi:10.1109/iceca.2019.8822052

Cited by 8 publications

(5 citation statements)

References 24 publications

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“…Moreover, poor image quality makes it difficult for the physician to diagnose and classify the different regions in the image using computer-based systems [2,3]. Hence, speckle filtering is an essential step in the pre-processing process of ultrasound images.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Fuzzy Logic Despeckling in Non-Subsampled Contourlet Transformed Ultrasound Pictures

Manikandan¹,

Karthikeyan²,

Babu³

et al. 2023

Intelligent Automation &Amp; Soft Computing

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Signal to noise ratio in ultrasound medical images captured through the digital camera is poorer, resulting in an inaccurate diagnosis. As a result, it needs an efficient despeckling method for ultrasound images in clinical practice and telemedicine. This article proposes a novel adaptive fuzzy filter based on the directionality and translation invariant property of the Non-Sub sampled Contour-let Transform (NSCT). Since speckle-noise causes fuzziness in ultrasound images, fuzzy logic may be a straightforward technique to derive the output from the noisy images. This filtering method comprises detection and filtering stages. First, image regions classify at the detection stage by applying fuzzy inference to the directional difference obtained from the NSCT noisy image. Then, the system adaptively selects the better-suited filter for the specific image region, resulting in significant speckle noise suppression and retention of detailed features. The suggested approach uses a weighted average filter to distinguish between noise and edges at the filtering stage. In addition, we apply a structural similarity measure as a tuning parameter depending on the kind of noise in the ultrasound pictures. The proposed methodology shows that the proposed fuzzy adaptive filter effectively suppresses speckle noise while preserving edges and image detailed structures compared to existing approaches.

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Section: Introductionmentioning

confidence: 99%

Adaptive Fuzzy Logic Despeckling in Non-Subsampled Contourlet Transformed Ultrasound Pictures

Manikandan¹,

Karthikeyan²,

Babu³

et al. 2023

Intelligent Automation &Amp; Soft Computing

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“…The framework starts by reducing the Rician and speckle noise from MRI to remove the unnecessary signal from MRI images. Reducing noise stages helps to reduce the loss of vital information in MRI, especially in small images [13][14][15][16]. In this paper, many algorithms have been used and modified to remove speckle and Rician noise, and their efficiency has been compared to other available filters.…”

Section: Introductionmentioning

confidence: 99%

Novel Framework of Segmentation 3D MRI of Brain Tumors

El-Henawy¹,

Elbaz²,

Ali³

et al. 2023

Computers, Materials &Amp; Continua

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Medical image segmentation is a crucial process for computer-aided diagnosis and surgery. Medical image segmentation refers to portioning the images into small, disjointed parts for simplifying the processes of analysis and examination. Rician and speckle noise are different types of noise in magnetic resonance imaging (MRI) that affect the accuracy of the segmentation process negatively. Therefore, image enhancement has a significant role in MRI segmentation. This paper proposes a novel framework that uses 3D MRI images from Kaggle and applies different diverse models to remove Rician and speckle noise using the best possible noise-free image. The proposed techniques consider the values of Peak Signal to Noise Ratio (PSNR) and the level of noise as inputs to the attention-U-Net model for segmentation of the tumor. The framework has been divided into three stages: removing speckle and Rician noise, the segmentation stage, and the feature extraction stage. The framework presents solutions for each problem at a different stage of the segmentation. In the first stage, the framework uses Vibrational Mode Decomposition (VMD) along with Block-matching and 3D filtering (Bm3D) algorithms to remove the Rician. Afterwards, the most significant Rician noise-free images are passed to the three different methods: Deep Residual Network (DeRNet), Dilated Convolution Auto-encoder Denoising Network (Di-Conv-AE-Net), and Denoising Generative Adversarial Network (DGAN-Net) for removing the speckle noise. VMD and Bm3D have achieved PSNR values for levels of noise (0, 0.25, 0.5, 0.75) for reducing the Rician

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“…This type of noise must be removed before the segmentation step. There are many types of algorithms are being developed to address the problem of speckle [9,10] This paper presents a new framework for the detection of breast tumors from ultrasound images that have speckle noise. The proposed framework contains a set of stages such as image enhancement, image segmentation, and classification.…”

Section: Introductionmentioning

confidence: 99%

“…) ln pF (m)(9) En = EnT + EnI + EnF(10) The next pseudocode shows the process of removing speckle noise from the breast ultrasound using the next pseudocodePseudocodeNeutrosopic Filter for denoisong Input: Noisy Ultrasound image Output: grayscale image 1-Transform noisy image to True, intermediacy and false set using neutrosophic 2-Calculate entropy for intermediacy set 3-Apply neutrosophic filter on True set to obtain T' 4-Check stopping criteria by comparing entropy with α using ENI(m+1)−ENI(m) ENI(m) < σ If stopping criteria met go to step 5 else set T = T' and go to step 3 6-convert t' from neutrosophic set to grayscale image…”

mentioning

confidence: 99%

Proposed Framework for Detection of Breast Tumors

Elbaz¹,

Elwahsh²,

El-Henawy³

2023

Computers, Materials &Amp; Continua

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Computer vision is one of the significant trends in computer science. It plays as a vital role in many applications, especially in the medical field. Early detection and segmentation of different tumors is a big challenge in the medical world. The proposed framework uses ultrasound images from Kaggle, applying five diverse models to denoise the images, using the best possible noise-free image as input to the U-Net model for segmentation of the tumor, and then using the Convolution Neural Network (CNN) model to classify whether the tumor is benign, malignant, or normal. The main challenge faced by the framework in the segmentation is the speckle noise. It's is a multiplicative and negative issue in breast ultrasound imaging, because of this noise, the image resolution and contrast become reduced, which affects the diagnostic value of this imaging modality. As result, speckle noise reduction is very vital for the segmentation process. The framework uses five models such as Generative Adversarial Denoising Network (DGAN-Net), Denoising U-Shaped Net (D-U-NET), Batch Renormalization U-Net (Br-U-NET), Generative Adversarial Network (GAN), and Nonlocal Neutrosophic of Wiener Filtering (NLNWF) for reducing the speckle noise from the breast ultrasound images then choose the best image according to peak signal to noise ratio (PSNR) for each level of speckle-noise. The five used methods have been compared with classical filters such as Bilateral, Frost, Kuan, and Lee and they proved their efficiency according to PSNR in different levels of noise. The five diverse models are achieved PSNR results for speckle noise at level (0.

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A Survey on Despeckling Filters for Speckle Noise Removal in Ultrasound Images

Cited by 8 publications

References 24 publications

Adaptive Fuzzy Logic Despeckling in Non-Subsampled Contourlet Transformed Ultrasound Pictures

Adaptive Fuzzy Logic Despeckling in Non-Subsampled Contourlet Transformed Ultrasound Pictures

Novel Framework of Segmentation 3D MRI of Brain Tumors

Proposed Framework for Detection of Breast Tumors

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