scholarly journals Noise Removal and Filtering Techniques used in Medical Images

2017 ◽  
Vol 10 (1) ◽  
pp. 103-113 ◽  
Author(s):  
Nalin Kumar ◽  
M Nachamai
Keyword(s):  
Medical Imaging ◽  
Gaussian Noise ◽  
Medical Images ◽  
Median Filter ◽  
Research Work ◽  
Noise Removal ◽  
Poisson Noise ◽  
Gaussian Filter ◽  
Gray Scale ◽  
Salt And Pepper

Noise removal techniques have become an essential practice in medical imaging application for the study of anatomical structure and image processing of MRI medical images. To report these issues many de-noising algorithm has been developed like Weiner filter, Gaussian filter, median filter etc. In this research work is done with only three of the above filters which are already mentioned were successfully used in medical imaging. The most commonly affected noises in medical MRI image are Salt and Pepper, Speckle, Gaussian and Poisson noise. The medical images taken for comparison include MRI images, in gray scale and RGB. The performances of these algorithms are examined for various noise types which are salt-and-pepper, Poisson, speckle, blurred and Gaussian Noise. The evaluation of these algorithms is done by the measures of the image file size, histogram and clarity scale of the images. The median filter performs better for removing salt-and-pepper noise and Poisson Noise for images in gray scale, and Weiner filter performs better for removing Speckle and Gaussian Noise and Gaussian filter for the Blurred Noise as suggested in the experimental results.

scholarly journals An Improved Method to Remove Salt and Pepper Noise in Noisy Images

2021 ◽  
Author(s):  
Jinder Kaur ◽  
Gurwinder Kaur ◽  
Ashwani Kumar
Keyword(s):  
Signal To Noise Ratio ◽  
Median Filter ◽  
Similarity Index ◽  
Noise Removal ◽  
Mean Value ◽  
Salt And Pepper Noise ◽  
Gray Scale ◽  
Noisy Pixel ◽  
Rgb Images ◽  
Salt And Pepper

In the field of image processing, removal of noise from Gray scale as well as RGB images is an ambitious task. The important function of noise removal algorithm is to eliminate noise from a noisy image. The salt and pepper noise (SPN) is frequently arising into Gray scale and RGB images while capturing, acquiring and transmitting over the insecure several communication mechanisms. In past, the numerous noise removal methods have been introduced to extract the noise from images adulterated with SPN. The proposed work introduces the SPN removal algorithm for Gray scale at low along with high density noise (10\% to 90\%). According to the different conditions of proposed algorithm, the noisy pixel is reconstructed by Winsorized mean or mean value of all pixels except the centre pixel which are present in the processing window. The noise from an image can be removed by using the proposed algorithm without degrading the quality of image. The performance evaluation of proposed and modified decision based unsymmetric median filter (MDBUTMF) is done on the basis of different performance parameters such as Peak Signal to Noise Ratio (PSNR), Mean Square Error (MSE), Image Enhancement Factor (IEF) and Structure Similarity Index Measurement (SSIM).

PERFORMANCE ANALYSIS OF NOISE REMOVAL TECHNIQUES FOR FACIAL IMAGES- A COMPARATIVE STUDY

2021 ◽  
Vol 12 (1) ◽  
pp. 1-10
Author(s):  
Anshika Jain ◽  
◽  
Maya Ingle
Keyword(s):  
Comparative Study ◽  
Gaussian Noise ◽  
Signal To Noise Ratio ◽  
Median Filter ◽  
Wiener Filter ◽  
Noise Removal ◽  
Poisson Noise ◽  
Signal To Noise ◽  
Mean Filter ◽  
Facial Images

Image de-noising has been a challenging issue in the field of digital image processing. It involves the manipulation of image data to produce a visually high quality image. While maintaining the desired information in the quality of an image, elimination of noise is an essential task. Various domain applications such as medical science, forensic science, text extraction, optical character recognition, face recognition, face detection etc. deal with noise removal techniques. There exist a variety of noises that may corrupt the images in different ways. Here, we explore filtering techniques viz. Mean filter, Median filter and Wiener filter to remove noises existing in facial images. The noises of our interest are namely; Gaussian noise, Salt & Pepper noise, Poisson noise and Speckle noise in our study. Further, we perform a comparative study based on the parameters such as Mean Square Error (MSE), Peak Signal to Noise Ratio (PSNR) and Structure Similarity Index Method (SSIM). For this research work, MATLAB R2013a on Labeled faces in Wild (lfw) database containing 120 facial images is used. Based upon the aforementioned parameters, we have attempted to analyze the performance of noise removal techniques with different types of noises. It has been observed that MSE, PSNR and SSIM for Mean filter are 44.19 with Poisson noise, 35.88 with Poisson noise and 0.197 with Gaussian noise respectively whereas for that of Median filter, these are 44.12 with Poisson noise, 46.56 with Salt & Pepper noise and 0.132 with Gaussian noise respectively. Wiener filter when contaminated with Poisson, Salt & Pepper and Gaussian noise, these parametric values are 44.52, 44.33 and 0.245 respectively. Based on these observations, we claim that the Median filtering technique works the best when contaminated with Poisson noise while the error strategy is dominant. On the other hand, Median filter also works the best with Salt & Pepper noise when Peak Signal to Noise Ratio is important. It is interesting to note that Median filter performs effectively with Gaussian noise using SSIM.

REMOVAL OF POISSON NOISE IN MEDICAL IMAGES USING FDCT INTEGRATED WITH RUDIN–OSHER–FATEMI MODEL

2014 ◽  
Vol 26 (06) ◽  
pp. 1450078
Author(s):  
D. Mary Sugantharathnam ◽  
D. Manimegalai
Keyword(s):  
Computed Tomography ◽  
Gaussian Noise ◽  
Medical Images ◽  
Noise Removal ◽  
Poisson Noise ◽  
The Other ◽  
Emission Computed Tomography ◽  
Clinical Images ◽  
Biomedical Images ◽  
Rof Model

This paper introduces a novel approach for accomplishing Poisson noise removal in biomedical images by multiresolution representation. Methods of denoising are described based on three classical methods: (1) Fast Discrete Curvelet Transform (FDCT) with simple soft thresholding, (2) Variance Stabilizing Transform (VST) combined with FDCT where hypothesis tests are made to detect the significant coefficients and (3) The proposed method where the FDCT is integrated with Rudin–Osher–Fatemi (ROF) model. Much of the literature has focused on developing algorithms for the removal of Gaussian noise where the estimation is often done by finding a Curvelet and by thresholding the noisy coefficients. However not much has been done to remove Poisson noise in biomedical images. But in most of the medical images, the recorded data are not modeled by Gaussian noise but is the realization of Poisson process. Hence, in this work, FDCT integrated with ROF model based on VST is proposed. The VST is applied so that the transformed data are homoscedastic and Gaussian. A classical hypothesis testing framework is used to detect the significant coefficients and an iterative scheme is used to reconstruct the final estimate. A central difference total variation term in the discrete ROF model is used. The model is experimented on a large number of clinical images like Computed Tomography (CT) images, X-Ray images, Positron Emission Tomography (PET) images and Single Photon Emission Computed Tomography (SPECT) images and the performances are evaluated in terms of Peak Signal to Noise Ratio (PSNR) and the Universal Quality Index (UQI). The results are compared with those obtained by the other two existing algorithms proposed in the literature. Numerical results show that the proposed algorithm obtains higher PSNR and UQI than the other two methods.

scholarly journals Medical Image Denoising Algorithm Based on Sparse Nonlocal Regularized Weighted Coding and Low Rank Constraint

2021 ◽  
Vol 2021 ◽  
pp. 1-6
Author(s):  
Quan Yuan ◽  
Zhenyun Peng ◽  
Zhencheng Chen ◽  
Yanke Guo ◽  
Bin Yang ◽  
...  
Keyword(s):  
Image Denoising ◽  
Gaussian Noise ◽  
Medical Image ◽  
Impulse Noise ◽  
Medical Images ◽  
Noise Removal ◽  
White Gaussian Noise ◽  
Low Rank ◽  
Rank Constraint ◽  
Nonlocal Regularization

Medical image information may be polluted by noise in the process of generation and transmission, which will seriously hinder the follow-up image processing and medical diagnosis. In medical images, there is a typical mixed noise composed of additive white Gaussian noise (AWGN) and impulse noise. In the conventional denoising methods, impulse noise is first removed, followed by the elimination of white Gaussian noise (WGN). However, it is difficult to separate the two kinds of noises completely in practical application. The existing denoising algorithm of weight coding based on sparse nonlocal regularization, which can simultaneously remove AWGN and impulse noise, is plagued by the problems of incomplete noise removal and serious loss of details. The denoising algorithm based on sparse representation and low rank constraint can preserve image details better. Thus, a medical image denoising algorithm based on sparse nonlocal regularization weighted coding and low rank constraint is proposed. The denoising effect of the proposed method and the original algorithm on computed tomography (CT) image and magnetic resonance (MR) image are compared. It is revealed that, under different σ and ρ values, the PSNR and FSIM values of CT and MRI images are evidently superior to those of traditional algorithms, suggesting that the algorithm proposed in this work has better denoising effects on medical images than traditional denoising algorithms.

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