scholarly journals Performance Analysis of Gaussian, Median, Mean and Weiner Filters on Biomedical Image De-Noising

2020 ◽  
Author(s):  
Subrato Bharati ◽  
Prajoy Podder
Keyword(s):  
Noise Reduction ◽  
Medical Image ◽  
Medical Images ◽  
Signal To Noise Ratio ◽  
Image Data ◽  
Speckle Noise ◽  
Ultrasound Images ◽  
Biomedical Image ◽  
Uniform Noise ◽  
Diagnostic Values

Noise reduction in medical images is a perplexing undertaking for the researchers in digital image processing. Noise generates maximum critical disturbances as well as touches the medical images quality, ultrasound images in the field of biomedical imaging. The image is normally considered as gathering of data and existence of noises degradation the image quality. It ought to be vital to reestablish the original image noises for accomplishing maximum data from images. Medical images are debased through noise through its transmission and procurement. Image with noise reduce the image contrast and resolution, thereby decreasing the diagnostic values of the medical image. This paper mainly focuses on Gaussian noise, Pepper noise, Uniform noise, Salt and Speckle noise. Different filtering techniques can be adapted for noise declining to improve the visual quality as well as reorganization of images. Here four types of noises have been undertaken and applied on medical images. Besides numerous filtering methods like Gaussian, median, mean and Weiner applied for noise reduction as well as estimate the performance of filter through the parameters like mean square error (MSE), peak signal to noise ratio (PSNR), Average difference value (AD) and Maximum difference value (MD) to diminish the noises without corrupting the medical image data.

scholarly journals Speckle and Rician Noise Removal from Medical Images and Ultrasound Images

2020 ◽  
Vol 8 (5) ◽  
pp. 1851-1854
Keyword(s):  
Noise Reduction ◽  
Medical Images ◽  
Signal To Noise Ratio ◽  
Median Filter ◽  
Speckle Noise ◽  
Noise Removal ◽  
Difficult Problem ◽  
Ultrasound Images ◽  
Simple Method ◽  
Different Types

In medical images, medical images are corrupted by different types of noise. It is important to get a precise picture and accurately observe the correspondence. Removing noise from medical images has become a very difficult problem in the field of the medical image. The most well-known noise reduction method, which is usually based on the local statistics of medical images, is efficient because of the noise reduction of medical images. In paper, an efficient and simple method for noise reduction from medical images is presented. The paper proposes a filtering system to combine both the Median filter and Gaussian filter to remove the Speckle noise form Medical and Ultrasound images. The image quality is measured through statistical quantities: Peak signal to noise ratio (PSNR). Experimental results show that the proposed system removes Speckle noise from medical images.

scholarly journals Fractional Calculus and Fuzzy Rule Based Filter for Despeckling the Medical Images

2018 ◽  
Vol 7 (4.10) ◽  
pp. 685
Author(s):  
Nageswari P ◽  
Rajan S ◽  
Manivel K
Keyword(s):  
Fractional Order ◽  
Medical Image ◽  
Medical Images ◽  
Signal To Noise Ratio ◽  
Medical Image Analysis ◽  
Ultrasound Image ◽  
Fuzzy Rule ◽  
Speckle Noise ◽  
Ultrasound Images ◽  
Medical Ultrasound

Medical ultrasound imaging plays an important role in diagnosis of various complicated disorders. But, these ultrasound images are intrinsically degraded with speckle noise which harshly affects the image visual qualities and essential particulars. Hence, denoising is an unavoidable process in medical image processing.  In this paper, a new despeckling technique is presented for denoising the medical ultrasound images by employing fuzzy technique on co-efficient of variation and fractional order integration filter. The proposed technique has two steps. During first step, the noisy image pixels are classified into three regions by using fuzzy technique on co-efficient of variation and consequently, the proposed technique adaptively employs appropriate filters on the grouped pixels to reduce noise in the ultrasound image. In the second step, to obtain an effective denoising image, the fractional order integration filter is applied on the resulting image of step 1. The performance of the proposed technique is tested on various medical images in terms of Peak signal to noise ratio and speckle suppression index quality measures. Experimental results reveal that the proposed despeckling technique can efficiently reduce the speckle noise, protect the edges and preserves any other important structural details of an image. It is suggested that the proposed technique is employed as a preprocessing tool for medical image analysis and diagnosis. 

scholarly journals A New Method to Remove Noise in Magnetic Resonance and Ultrasound Images

2010 ◽  
Vol 3 (1) ◽  
pp. 81 ◽  
Author(s):  
M. A. Yousuf ◽  
M. N. Nobi
Keyword(s):  
Image Processing ◽  
Magnetic Resonance ◽  
Noise Reduction ◽  
Medical Image ◽  
Medical Images ◽  
Signal To Noise Ratio ◽  
Ultrasound Image ◽  
Medical Image Processing ◽  
Experimental Results ◽  
Signal To Noise

In medical image processing, medical images are corrupted by different type of noises. It is very important to obtain precise images to facilitate accurate observations for the given application. Removing of noise from medical images is now a very challenging issue in the field of medical image processing. Most well known noise reduction methods, which are usually based on the local statistics of a medical image, are not efficient for medical image noise reduction. This paper presents an efficient and simple method for noise reduction from medical images. In the proposed method median filter is modified by adding more features. Experimental results are also compared with the other three image filtering algorithms. The quality of the output images is measured by the statistical quantity measures: peak signal-to-noise ratio (PSNR), signal-to-noise ratio (SNR) and root mean square error (RMSE). Experimental results of magnetic resonance (MR) image and ultrasound image demonstrate that the proposed algorithm is comparable to popular image smoothing algorithms.Key words: Magnetic resonance image; Ultrasound image; PSNR; SNR; RMSE.© 2011 JSR Publications. ISSN: 2070-0237 (Print); 2070-0245 (Online). All rights reserved.doi:10.3329/jsr.v3i1.5544                J. Sci. Res. 3 (1), 81-89 (2011)

scholarly journals MFNR: Multi-Frame Method for Complete Speckle Noise Removal in Ultrasound images Using KDE

2020 ◽  
Author(s):  
Awais Nazir ◽  
Muhammad Shahzad Younis ◽  
Muhammad Khurram Shahzad
Keyword(s):  
Signal To Noise Ratio ◽  
Low Cost ◽  
Speckle Noise ◽  
Noise Removal ◽  
General Purpose ◽  
Imaging Systems ◽  
Ultrasound Images ◽  
Frame Method ◽  
Probability Density Function Pdf ◽  
Removal System

Speckle noise is one of the most difficult noises to remove especially in medical applications. It is a nuisance in ultrasound imaging systems which is used in about half of all medical screening systems. Thus, noise removal is an important step in these systems, thereby creating reliable, automated, and potentially low cost systems. Herein, a generalized approach MFNR (Multi-Frame Noise Removal) is used, which is a complete Noise Removal system using KDE (Kernal Density Estimation). Any given type of noise can be removed if its probability density function (PDF) is known. Herein, we extracted the PDF parameters using KDE. Noise removal and detail preservation are not contrary to each other as the case in single-frame noise removal methods. Our results showed practically complete noise removal using MFNR algorithm compared to standard noise removal tools. The Peak Signal to Noise Ratio (PSNR) performance was used as a comparison metric. This paper is an extension to our previous paper where MFNR Algorithm was showed as a general purpose complete noise removal tool for all types of noises

scholarly journals A Fast, Fully Automated Prostate Boundary Segmentation Using Probabilistic Approaches In Ultrasound Images

2021 ◽  
Author(s):  
Rasa Vafaie
Keyword(s):  
Em Algorithm ◽  
Signal To Noise Ratio ◽  
Transrectal Ultrasound ◽  
Speckle Noise ◽  
Computational Time ◽  
Dice Similarity Coefficient ◽  
Ultrasound Images ◽  
Segmentation Method ◽  
Initial Model ◽  
Hybrid Segmentation

Segmentation of prostate boundaries in transrectal ultrasound (TRUS) images plays a great role in prostate cancer diagnosis. Due to the low signal to noise ratio and existence of the speckle noise in TRUS images, prostate image segmentation has proven to be an extremely difficult task. In this thesis report, a fast fully automated hybrid segmentation method based on probabilistic approaches is presented. First, the position of the initial model is automatically estimated using prostate boundary representative patterns. Next, the Expectation Maximization (EM) algorithm and Markov Random Field (MRF) theory are utilized in the deformation strategy to optimally fit the initial model on the prostate boundaries. A less computationally EM algorithm and a new surface smoothing technique are proposed to decrease the segmentation time. Successful experimental results with the average Dice Similarity Coefficient (DSC) value 93.9±2.7% and computational time around 9 seconds validate the algorithm.

Denoising Ultrasound Medical Images

2017 ◽  
pp. 761-775
Author(s):  
A.S.C.S. Sastry ◽  
P.V.V. Kishore ◽  
Ch. Raghava Prasad ◽  
M.V.D. Prasad
Keyword(s):  
Image Quality ◽  
Medical Images ◽  
Ultrasound Image ◽  
Similarity Index ◽  
Speckle Noise ◽  
Ultrasound Images ◽  
Medical Ultrasound ◽  
Soft Thresholding ◽  
Block Based

Medical ultrasound imaging has revolutioned the diagnostics of human body in the last few decades. The major drawback of ultrasound medical images is speckle noise. Speckle noise in ultrasound images is because of multiple reflections of ultrasound waves from hard tissues. Speckle noise degrades the medical ultrasound images lessening the visible quality of the image. The aim of this paper is to improve the image quality of ultrasound medical images by applying block based hard and soft thresholding on wavelet coefficients. Medical ultrasound image transformation to wavelet domain uses debauchee's mother wavelet. Divide the approximate and detailed coefficients into uniform blocks of size 8×8, 16×16, 32×32 and 64×64. Hard and soft thresholding on these blocks of approximate and detailed coefficients reduces speckle noise. Inverse transformation to original spatial domain produces a noise reduced ultrasound image. Experiments on medical ultrasound images obtained from diagnostic centers in Vijayawada, India show good improvements to ultrasound images visually. Quality of improved images in measured using peak signal to noise ratio (PSNR), image quality index (IQI), structural similarity index (SSIM).

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