1. The document discusses efficient analysis of medical image de-noising for MRI and ultrasound images. It investigates three filters: median, Gaussian, and Wiener filters.
2. It provides background on noise in medical images and summarizes previous research on de-noising algorithms for different image modalities.
3. The mathematical background section explains how the median, Gaussian, and Wiener filters work for noise removal. It also defines peak signal-to-noise ratio (PSNR) to evaluate de-noising outcomes.
Images of different body organs play very important role in medical diagnosis. Images can be taken
by using different techniques like x-rays, gamma rays, ultrasound etc. Ultrasound images are widely used
as a diagnosis tool because of its non invasive nature and low cost. The medical images which uses the
principle of coherence suffers from speckle noise, which is multiplicative in nature. Ultrasound images are
coherent images so speckle noise is inherited in ultrasound images which occur at the time of image
acquisition. There are many factors which can degrade the quality of image but noise present in ultrasound
image is a prime factor which can negatively affect result while autonomous machine perception. In this
paper we will discuss types of noises and speckle reduction techniques. In the end, study about speckle
reduction in ultrasound of various researchers will be compared.
This document summarizes a research paper that proposes a new method for removing speckle noise from ultrasound and optical coherence tomography medical images in the stationary wavelet domain. It first reviews existing techniques for speckle noise reduction such as wavelet shrinkage methods. It then presents the mathematical model of speckle noise and formulates the problem that existing wavelet methods do not provide shift invariance. The proposed method uses two-dimensional stationary wavelet transform to overcome this issue. It involves decomposing the noisy input image into subbands, estimating clean coefficients, and applying the inverse transform to obtain a denoised image. Results showed the method was able to remove speckle noise while better preserving edges.
Performance of Various Order Statistics Filters in Impulse and Mixed Noise Re...sipij
Remote sensing images (ranges from satellite to seismic) are affected by number of noises like interference, impulse and speckle noises. Image denoising is one of the traditional problems in digital image processing, which plays vital role as a pre-processing step in number of image and video applications. Image denoising still remains a challenging research area for researchers because noise
removal introduces artifacts and causes blurring of the images. This study is done with the intension of designing a best algorithm for impulsive noise reduction in an industrial environment. A review of the typical impulsive noise reduction systems which are based on order statistics are done and particularized for the described situation. Finally, computational aspects are analyzed in terms of PSNR values and some solutions are proposed.
This document evaluates various filtering techniques for reducing speckle noise in ultrasound images. It first describes common noise filtering algorithms like median filtering, average filtering, and Wiener filtering. It then evaluates hybrid combinations of these filters on ultrasound images. Performance is quantified using metrics like mean squared error, signal-to-noise ratio, peak signal-to-noise ratio, speckle index, and edge preservation index. Experimental results on a sample pancreas image show that average filtering with a 3x3 window and hybrid combinations of filters like Butterworth filtering followed by Wiener filtering can effectively reduce speckle noise while preserving image details.
IRJET- Analysis of Various Noise Filtering Techniques for Medical ImagesIRJET Journal
This document discusses various noise filtering techniques used for medical images. It begins with an introduction to different types of noise that can affect medical images during acquisition, like salt and pepper noise, Gaussian noise, shot noise, and anisotropic noise. It then describes common filtering techniques used to remove noise, such as mean filtering, median filtering, Gaussian filtering, and Wiener filtering. The document provides examples of how these different filtering techniques can be applied to tackle specific noise issues. It also includes a literature review comparing different noise removal algorithms and their features/limitations. Overall, the document analyzes noise issues in medical images and various filtering strategies that can be used to enhance image quality.
Novel adaptive filter (naf) for impulse noise suppression from digital imagesijbbjournal
In general, it is known that an adaptive filter adjusts its parameters iteratively such as size of the working
window, decision threshold values used in two stage detection-estimation based switching filters, number of
iterations etc. It is also known that nonlinear filters such as median filters and its several variants are
popularly known for their ability in dealing with the unknown circumstances. In this paper an efficient and
simple adaptive nonlinear filtering scheme is presented to eliminate the impulse noise from the digital images with an impulsive noise detection and reduction scheme based on adaptive nonlinear filter techniques. The proposed scheme employs image statistics based dynamically varying working window and an adaptive threshold for noise detection with a Noise Exclusive Median (NEM) based restoration. The intensity value of the Noise Exclusive Median (NEM) is derived from the processed pixels in local
neighborhood of a dynamically adaptive window. In the proposed scheme use of an adaptive threshold value derived from the noisy image statistics returns more precise results for the noisy pixel detection. The
proposed scheme is simple and can be implemented as either a single pass or a multi-pass with a maximum
of three iterations with a simple stopping criterion. The goodness of the proposed scheme is evaluated with respect to the qualitative and quantitative measures obtained by MATLAB simulations with standard images added with impulsive noise of varying densities. From the comparative analysis it is evident that the proposed scheme out performs the state-of-art schemes, preferably in cases of high-density impulse noise
An adaptive threshold segmentation for detection of nuclei in cervical cells ...csandit
PAP smear test is the most efficient and easy procedure to detect any abnormality in cervical
cells. It becomes difficult for the cytologist to analyse a large set of PAP smear test images
when there is a rapid increase in the incidence of cervical cancer. On the replacement, image
analysis could swap manual interpretation. This paper proposes a method for the detection of
cervical cells in pap smear images using wavelet based thresholding. First, Wiener filter is used
for smoothing to suppress the noise and to improve the contrast of the image. Second, optimal
threshold is been obtained for segmenting the cell by various Wavelet shrinkage techniques like
VisuShrink, BayesShrink and SureShrink thresholding which segment the foreground from the
background and detect cell component like nucleus from the clustered cell images. From the
results, it is proved that the performance of the adaptive Wiener filter with combination of
SureShrink thresholding performs better in terms of threshold values and Mean Squared Error
than the other comparative methods. The succeeding research work can be carried out based on
the size of the segmented nucleus which therefore helps in differentiating abnormality among
the cells.
Images of different body organs play very important role in medical diagnosis. Images can be taken
by using different techniques like x-rays, gamma rays, ultrasound etc. Ultrasound images are widely used
as a diagnosis tool because of its non invasive nature and low cost. The medical images which uses the
principle of coherence suffers from speckle noise, which is multiplicative in nature. Ultrasound images are
coherent images so speckle noise is inherited in ultrasound images which occur at the time of image
acquisition. There are many factors which can degrade the quality of image but noise present in ultrasound
image is a prime factor which can negatively affect result while autonomous machine perception. In this
paper we will discuss types of noises and speckle reduction techniques. In the end, study about speckle
reduction in ultrasound of various researchers will be compared.
This document summarizes a research paper that proposes a new method for removing speckle noise from ultrasound and optical coherence tomography medical images in the stationary wavelet domain. It first reviews existing techniques for speckle noise reduction such as wavelet shrinkage methods. It then presents the mathematical model of speckle noise and formulates the problem that existing wavelet methods do not provide shift invariance. The proposed method uses two-dimensional stationary wavelet transform to overcome this issue. It involves decomposing the noisy input image into subbands, estimating clean coefficients, and applying the inverse transform to obtain a denoised image. Results showed the method was able to remove speckle noise while better preserving edges.
Performance of Various Order Statistics Filters in Impulse and Mixed Noise Re...sipij
Remote sensing images (ranges from satellite to seismic) are affected by number of noises like interference, impulse and speckle noises. Image denoising is one of the traditional problems in digital image processing, which plays vital role as a pre-processing step in number of image and video applications. Image denoising still remains a challenging research area for researchers because noise
removal introduces artifacts and causes blurring of the images. This study is done with the intension of designing a best algorithm for impulsive noise reduction in an industrial environment. A review of the typical impulsive noise reduction systems which are based on order statistics are done and particularized for the described situation. Finally, computational aspects are analyzed in terms of PSNR values and some solutions are proposed.
This document evaluates various filtering techniques for reducing speckle noise in ultrasound images. It first describes common noise filtering algorithms like median filtering, average filtering, and Wiener filtering. It then evaluates hybrid combinations of these filters on ultrasound images. Performance is quantified using metrics like mean squared error, signal-to-noise ratio, peak signal-to-noise ratio, speckle index, and edge preservation index. Experimental results on a sample pancreas image show that average filtering with a 3x3 window and hybrid combinations of filters like Butterworth filtering followed by Wiener filtering can effectively reduce speckle noise while preserving image details.
IRJET- Analysis of Various Noise Filtering Techniques for Medical ImagesIRJET Journal
This document discusses various noise filtering techniques used for medical images. It begins with an introduction to different types of noise that can affect medical images during acquisition, like salt and pepper noise, Gaussian noise, shot noise, and anisotropic noise. It then describes common filtering techniques used to remove noise, such as mean filtering, median filtering, Gaussian filtering, and Wiener filtering. The document provides examples of how these different filtering techniques can be applied to tackle specific noise issues. It also includes a literature review comparing different noise removal algorithms and their features/limitations. Overall, the document analyzes noise issues in medical images and various filtering strategies that can be used to enhance image quality.
Novel adaptive filter (naf) for impulse noise suppression from digital imagesijbbjournal
In general, it is known that an adaptive filter adjusts its parameters iteratively such as size of the working
window, decision threshold values used in two stage detection-estimation based switching filters, number of
iterations etc. It is also known that nonlinear filters such as median filters and its several variants are
popularly known for their ability in dealing with the unknown circumstances. In this paper an efficient and
simple adaptive nonlinear filtering scheme is presented to eliminate the impulse noise from the digital images with an impulsive noise detection and reduction scheme based on adaptive nonlinear filter techniques. The proposed scheme employs image statistics based dynamically varying working window and an adaptive threshold for noise detection with a Noise Exclusive Median (NEM) based restoration. The intensity value of the Noise Exclusive Median (NEM) is derived from the processed pixels in local
neighborhood of a dynamically adaptive window. In the proposed scheme use of an adaptive threshold value derived from the noisy image statistics returns more precise results for the noisy pixel detection. The
proposed scheme is simple and can be implemented as either a single pass or a multi-pass with a maximum
of three iterations with a simple stopping criterion. The goodness of the proposed scheme is evaluated with respect to the qualitative and quantitative measures obtained by MATLAB simulations with standard images added with impulsive noise of varying densities. From the comparative analysis it is evident that the proposed scheme out performs the state-of-art schemes, preferably in cases of high-density impulse noise
An adaptive threshold segmentation for detection of nuclei in cervical cells ...csandit
PAP smear test is the most efficient and easy procedure to detect any abnormality in cervical
cells. It becomes difficult for the cytologist to analyse a large set of PAP smear test images
when there is a rapid increase in the incidence of cervical cancer. On the replacement, image
analysis could swap manual interpretation. This paper proposes a method for the detection of
cervical cells in pap smear images using wavelet based thresholding. First, Wiener filter is used
for smoothing to suppress the noise and to improve the contrast of the image. Second, optimal
threshold is been obtained for segmenting the cell by various Wavelet shrinkage techniques like
VisuShrink, BayesShrink and SureShrink thresholding which segment the foreground from the
background and detect cell component like nucleus from the clustered cell images. From the
results, it is proved that the performance of the adaptive Wiener filter with combination of
SureShrink thresholding performs better in terms of threshold values and Mean Squared Error
than the other comparative methods. The succeeding research work can be carried out based on
the size of the segmented nucleus which therefore helps in differentiating abnormality among
the cells.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
This document discusses a method for reducing noise in ultrasound images using wavelet thresholding. Ultrasound images are often corrupted by speckle noise, which degrades image quality and obscures details. The proposed method uses wavelet transforms to represent the image at different scales, followed by thresholding of the wavelet coefficients to suppress noise while retaining important details. The threshold value and level of wavelet decomposition must be optimized to remove noise without excessively smoothing important textures. Experimental results on ultrasound images show that the proposed wavelet thresholding method can improve noise suppression compared to the original noisy images, as measured by metrics like SNR and PSNR.
This document provides an overview of image denoising techniques. It discusses different types of noise that can affect images, such as amplifier noise, impulsive noise, and speckle noise. It also describes various denoising methodologies, including spatial filtering techniques like mean and median filters, as well as transform domain filtering and wavelet thresholding. Spatial filters can smooth noise but also blur edges, while wavelet thresholding can preserve edges while removing noise. The document reviews noise models, denoising methods, and provides insights to determine the most effective approach based on the noise characteristics.
Survey on Noise Removal in Digital ImagesIOSR Journals
This document summarizes several algorithms for removing noise from digital images. It focuses on three common types of noise (impulse, speckle, and Gaussian noise) and three types of images (sensor, medical, and grayscale). For each noise/image combination, several filtering algorithms are described and compared based on their ability to remove noise while preserving important image details. The document concludes that the best algorithm depends on the specific noise and image type, and suggests the need for further research to identify optimal noise removal methods.
An Efficient Image Denoising Approach for the Recovery of Impulse NoisejournalBEEI
Image noise is one of the key issues in image processing applications today. The noise will affect the quality of the image and thus degrades the actual information of the image. Visual quality is the prerequisite for many imagery applications such as remote sensing. In recent years, the significance of noise assessment and the recovery of noisy images are increasing. The impulse noise is characterized by replacing a portion of an image’s pixel values with random values Such noise can be introduced due to transmission errors. Accordingly, this paper focuses on the effect of visual quality of the image due to impulse noise during the transmission of images. In this paper, a hybrid statistical noise suppression technique has been developed for improving the quality of the impulse noisy color images. We further proved the performance of the proposed image enhancement scheme using the advanced performance metrics.
Survey Paper on Image Denoising Using Spatial Statistic son PixelIJERA Editor
This document summarizes research on image denoising using spatial statistics on pixel values. It begins with an abstract describing an approach that uses adaptive anisotropic weighted similarity functions between local neighborhoods derived from Mexican Hat wavelets to improve perceptual quality over existing methods. It then reviews literature on various denoising techniques including non-local means, non-uniform triangular partitioning, undecimated wavelet transforms, anisotropic diffusion, and support vector regression. Key types of image noise like Gaussian, salt and pepper, Poisson, and speckle noise are described. Limitations of blurring and noise in digital images are discussed. In conclusion, the document provides an overview of image denoising research using spatial and transform domain techniques.
The Performance Analysis of Median Filter for Suppressing Impulse Noise from ...iosrjce
IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
International Journal of Image Processing (IJIP) Volume (1) Issue (1)CSCJournals
This document discusses a new method for enhancing magnetic resonance images (MRI) using multiscale retinex (MSR) to correct image inhomogeneities and enhance contrast. MSR employs single-scale retinex with different weightings to address non-uniform grayscale, improve contrast, correct inhomogeneity, and clarify deep brain structures in MRIs captured using surface coils. The performance is evaluated using peak signal-to-noise ratio and contrast-to-noise ratio on phantom and animal images, and is shown to outperform other methods like histogram equalization and wavelet-based algorithms. The retinex algorithm is thus a valuable method for MRI image processing.
This document summarizes a research paper that compares different methods for reducing speckle noise in ultrasound images of the kidney. It first provides background on speckle noise and how it affects ultrasound image quality. It then describes several existing speckle reduction techniques, including median filtering, Wiener filtering, and wavelet-based methods. The main goal of the presented study is to quantitatively and qualitatively compare enhanced ultrasound images produced by different noise reduction methods, with the aim of improving image quality for diagnosis. Wavelet transforms are identified as a promising approach due to their ability to separate noise from image features across frequency scales. Experimental results applying several filters to kidney ultrasound images are analyzed to determine the best noise reduction performance.
Analysis PSNR of High Density Salt and Pepper Impulse Noise Using Median Filterijtsrd
In this paper a new method for the enhancement of gray scale images is introduced, when images are corrupted by fixed valued impulse noise salt and pepper noise . The proposed methodology ensures a better output for low and medium density of fixed value impulse noise as compare to the other famous filters like Standard Median Filter SMF , Decision Based Median Filter DBMF and Modified Decision Based Median Filter MDBMF etc. The main objective of the proposed method was to improve peak signal to noise ratio PSNR , visual perception and reduction in blurring of image. The proposed algorithm replaced the noisy pixel by trimmed mean value. When previous pixel values, 0's and 255's are present in the particular window and all the pixel values are 0's and 255's then the remaining noisy pixels are replaced by mean value. The gray-scale image of mandrill and Lena were tested via proposed method. The experimental result shows better peak signal to noise ratio PSNR , mean square error MSE and mean absolute error MAE values with better visual and human perception. Sonali Malviya | Prof. Anshuj Jain "Analysis PSNR of High Density Salt and Pepper Impulse Noise Using Median Filter" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-1 , December 2018, URL: http://www.ijtsrd.com/papers/ijtsrd19086.pdf
http://www.ijtsrd.com/engineering/computer-engineering/19086/analysis-psnr-of-high-density-salt-and-pepper-impulse-noise-using-median-filter/sonali-malviya
EDGE PRESERVATION OF ENHANCED FUZZY MEDIAN MEAN FILTER USING DECISION BASED M...ijsc
Image noise refers to random variations in the basic characteristics of image like brightness, intensity or
color difference. These variations are not present in the image which is captured but may occur due to
environmental conditions like sensor temperature or due to circuit of the scanner or other similar issues.
Basically noise means unwanted signals in the image. Various filters have been designed for removal of
almost all types of noise. It has been seen in most of the cases that as a result of high amount of filtering or
repetitive filtering of image for the removal of noise, edges of images mostly get distorted or smeared out. It
means that most of the filtering techniques lead to loss of fine edges of the images which needs to be
preserved in order to enhance the quality of image. This paper has focused on to improve the enhanced
fuzzy median mean filter so that fine edges get preserved in a better way. Experiments have been performed
in MATLAB. Comparative analysis have been done on the basis of PSNR, MSE, BER and RMSE and it has
shown that border correction applied on images improves the results of enhanced fuzzy median mean filter.
Image Restoration Using Particle Filters By Improving The Scale Of Texture Wi...CSCJournals
Traditional techniques are based on restoring image values based on local smoothness constraints within fixed bandwidth windows where image structure is not considered. Common problem for such methods is how to choose the most appropriate bandwidth and the most suitable set of neighboring pixels to guide the reconstruction process. The present work proposes a denoising technique based on particle filtering using MRF (Markov Random Field). It is an automatic technique to capture the scale of texture. The contribution of our method is the selection of an appropriate window in the image domain. For this we first construct a set containing all occurrences then the conditional pdf can be estimated with a histogram of all center pixel values. Particle evolution is controlled by the image structure leading to a filtering window adapted to the image content. Our method explores multiple neighbors’ sets (or hypotheses) that can be used for pixel denoising, through a particle filtering approach. This technique associates weights for each hypothesis according to its relevance and its contribution in the denoising process.
This document summarizes a research paper that compares different image filtering methods for reducing noise, including an adaptive bilateral filter, median filter, and Butterworth filter. The paper applies these filters to images with added Gaussian white noise and compares the results based on visual quality, mean squared error (MSE), and peak signal-to-noise ratio (PSNR). It finds that the adaptive bilateral filter produces the best results with the lowest MSE and highest PSNR, indicating it most effectively removes noise while preserving image details and sharpness.
The document proposes a hybrid approach for segmenting brain tumors in MRI images using wavelet and watershed transforms. It begins with applying wavelet transform to produce approximation and detail images for noise reduction. Edge detection is then performed on the approximation image. Watershed transform is applied for initial segmentation at low resolution. Repeated inverse wavelet transform is used to increase the segmented image resolution. Region merging is applied for further segmentation refinement before cropping the tumor area. The results show this coactive wavelet-watershed approach can help achieve accurate tumor segmentation.
Correction of Inhomogeneous MR Images Using Multiscale RetinexCSCJournals
A new method for enhancing the contrast of magnetic resonance images (MRI) by retinex algorithm is proposed. It can correct the blurrings in deep anatomical structures and inhomogeneity of MRI. Multiscale retinex (MSR) employed SSR with different weightings to correct inhomogeneities and enhance the contrast of MR images. The method was assessed by applying it to phantom and animal images acquired on MRI scanner systems. Its performance was also compared with other methods based on two indices: (1) the peak signal-to-noise ratio (PSNR) and (2) the contrast-to-noise ratio (CNR). Two indices, including PSNR and CNR, were used to evaluate the performance of correction of inhomogeneity in MR images. The PSNR/CNR of a phantom and animal images were 11.8648 dB/2.0922 and 11.7580 dB/2.1157, respectively, which were higher or very close to the results of wavelet algorithm. The retinex algorithm successfully corrected a nonuniform grayscale, enhanced contrast, corrected inhomogeneity, and clarified the deep brain structures of MR images captured by surface coils and outperformed histogram equalization, local histogram equalization, and a waveletbased algorithm, and hence may be a valuable method in MR image processing.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
Image Denoising is an important part of diverse image processing and computer vision problems. The
important property of a good image denoising model is that it should completely remove noise as far as
possible as well as preserve edges. One of the most powerful and perspective approaches in this area is
image denoising using discrete wavelet transform (DWT). In this paper, comparison of various Wavelets at
different decomposition levels has been done. As number of levels increased, Peak Signal to Noise Ratio
(PSNR) of image gets decreased whereas Mean Absolute Error (MAE) and Mean Square Error (MSE) get
increased . A comparison of filters and various wavelet based methods has also been carried out to denoise
the image. The simulation results reveal that wavelet based Bayes shrinkage method outperforms other
methods.
Airtel leveraged its sponsorship of the Champions League T20 cricket tournament in India to engage fans on YouTube. It held a contest asking fans to make and upload cheering videos for their favorite teams, with the top 10 most viewed videos winning a trip to the tournament finals in South Africa. The campaign was a success, receiving over 2,500 video submissions and making the Airtel YouTube channel the most subscribed in India that month. It established Airtel as supporting fans' passion and talent while generating engagement through social media.
This document discusses a Facebook advertising campaign for Manchester United Café Bar. It mentions creating a sponsored ad and Facebook page to promote a live event featuring Ronaldo versus Rooney at the café bar. The document thanks the reader, suggesting help was provided for the small business's Facebook marketing.
Kiehl's presentation - The Three Kiehler'sJohann Fromont
This document outlines a pre-case study for Kiehl's branding strategy in the men's skincare market. It begins with an analysis of market trends, competitors, Kiehl's target consumer, and a SWOT analysis. Opportunities for Kiehl's include expanding in the men's market and emerging markets. The proposed product is a Facial Fuel Energizing Tonic for Men. The marketing strategy discusses the product, place, price and promotion approaches, focusing on a social media campaign around a "Kiehl's Epic Journey" and developing an informational app.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
This document discusses a method for reducing noise in ultrasound images using wavelet thresholding. Ultrasound images are often corrupted by speckle noise, which degrades image quality and obscures details. The proposed method uses wavelet transforms to represent the image at different scales, followed by thresholding of the wavelet coefficients to suppress noise while retaining important details. The threshold value and level of wavelet decomposition must be optimized to remove noise without excessively smoothing important textures. Experimental results on ultrasound images show that the proposed wavelet thresholding method can improve noise suppression compared to the original noisy images, as measured by metrics like SNR and PSNR.
This document provides an overview of image denoising techniques. It discusses different types of noise that can affect images, such as amplifier noise, impulsive noise, and speckle noise. It also describes various denoising methodologies, including spatial filtering techniques like mean and median filters, as well as transform domain filtering and wavelet thresholding. Spatial filters can smooth noise but also blur edges, while wavelet thresholding can preserve edges while removing noise. The document reviews noise models, denoising methods, and provides insights to determine the most effective approach based on the noise characteristics.
Survey on Noise Removal in Digital ImagesIOSR Journals
This document summarizes several algorithms for removing noise from digital images. It focuses on three common types of noise (impulse, speckle, and Gaussian noise) and three types of images (sensor, medical, and grayscale). For each noise/image combination, several filtering algorithms are described and compared based on their ability to remove noise while preserving important image details. The document concludes that the best algorithm depends on the specific noise and image type, and suggests the need for further research to identify optimal noise removal methods.
An Efficient Image Denoising Approach for the Recovery of Impulse NoisejournalBEEI
Image noise is one of the key issues in image processing applications today. The noise will affect the quality of the image and thus degrades the actual information of the image. Visual quality is the prerequisite for many imagery applications such as remote sensing. In recent years, the significance of noise assessment and the recovery of noisy images are increasing. The impulse noise is characterized by replacing a portion of an image’s pixel values with random values Such noise can be introduced due to transmission errors. Accordingly, this paper focuses on the effect of visual quality of the image due to impulse noise during the transmission of images. In this paper, a hybrid statistical noise suppression technique has been developed for improving the quality of the impulse noisy color images. We further proved the performance of the proposed image enhancement scheme using the advanced performance metrics.
Survey Paper on Image Denoising Using Spatial Statistic son PixelIJERA Editor
This document summarizes research on image denoising using spatial statistics on pixel values. It begins with an abstract describing an approach that uses adaptive anisotropic weighted similarity functions between local neighborhoods derived from Mexican Hat wavelets to improve perceptual quality over existing methods. It then reviews literature on various denoising techniques including non-local means, non-uniform triangular partitioning, undecimated wavelet transforms, anisotropic diffusion, and support vector regression. Key types of image noise like Gaussian, salt and pepper, Poisson, and speckle noise are described. Limitations of blurring and noise in digital images are discussed. In conclusion, the document provides an overview of image denoising research using spatial and transform domain techniques.
The Performance Analysis of Median Filter for Suppressing Impulse Noise from ...iosrjce
IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
International Journal of Image Processing (IJIP) Volume (1) Issue (1)CSCJournals
This document discusses a new method for enhancing magnetic resonance images (MRI) using multiscale retinex (MSR) to correct image inhomogeneities and enhance contrast. MSR employs single-scale retinex with different weightings to address non-uniform grayscale, improve contrast, correct inhomogeneity, and clarify deep brain structures in MRIs captured using surface coils. The performance is evaluated using peak signal-to-noise ratio and contrast-to-noise ratio on phantom and animal images, and is shown to outperform other methods like histogram equalization and wavelet-based algorithms. The retinex algorithm is thus a valuable method for MRI image processing.
This document summarizes a research paper that compares different methods for reducing speckle noise in ultrasound images of the kidney. It first provides background on speckle noise and how it affects ultrasound image quality. It then describes several existing speckle reduction techniques, including median filtering, Wiener filtering, and wavelet-based methods. The main goal of the presented study is to quantitatively and qualitatively compare enhanced ultrasound images produced by different noise reduction methods, with the aim of improving image quality for diagnosis. Wavelet transforms are identified as a promising approach due to their ability to separate noise from image features across frequency scales. Experimental results applying several filters to kidney ultrasound images are analyzed to determine the best noise reduction performance.
Analysis PSNR of High Density Salt and Pepper Impulse Noise Using Median Filterijtsrd
In this paper a new method for the enhancement of gray scale images is introduced, when images are corrupted by fixed valued impulse noise salt and pepper noise . The proposed methodology ensures a better output for low and medium density of fixed value impulse noise as compare to the other famous filters like Standard Median Filter SMF , Decision Based Median Filter DBMF and Modified Decision Based Median Filter MDBMF etc. The main objective of the proposed method was to improve peak signal to noise ratio PSNR , visual perception and reduction in blurring of image. The proposed algorithm replaced the noisy pixel by trimmed mean value. When previous pixel values, 0's and 255's are present in the particular window and all the pixel values are 0's and 255's then the remaining noisy pixels are replaced by mean value. The gray-scale image of mandrill and Lena were tested via proposed method. The experimental result shows better peak signal to noise ratio PSNR , mean square error MSE and mean absolute error MAE values with better visual and human perception. Sonali Malviya | Prof. Anshuj Jain "Analysis PSNR of High Density Salt and Pepper Impulse Noise Using Median Filter" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-1 , December 2018, URL: http://www.ijtsrd.com/papers/ijtsrd19086.pdf
http://www.ijtsrd.com/engineering/computer-engineering/19086/analysis-psnr-of-high-density-salt-and-pepper-impulse-noise-using-median-filter/sonali-malviya
EDGE PRESERVATION OF ENHANCED FUZZY MEDIAN MEAN FILTER USING DECISION BASED M...ijsc
Image noise refers to random variations in the basic characteristics of image like brightness, intensity or
color difference. These variations are not present in the image which is captured but may occur due to
environmental conditions like sensor temperature or due to circuit of the scanner or other similar issues.
Basically noise means unwanted signals in the image. Various filters have been designed for removal of
almost all types of noise. It has been seen in most of the cases that as a result of high amount of filtering or
repetitive filtering of image for the removal of noise, edges of images mostly get distorted or smeared out. It
means that most of the filtering techniques lead to loss of fine edges of the images which needs to be
preserved in order to enhance the quality of image. This paper has focused on to improve the enhanced
fuzzy median mean filter so that fine edges get preserved in a better way. Experiments have been performed
in MATLAB. Comparative analysis have been done on the basis of PSNR, MSE, BER and RMSE and it has
shown that border correction applied on images improves the results of enhanced fuzzy median mean filter.
Image Restoration Using Particle Filters By Improving The Scale Of Texture Wi...CSCJournals
Traditional techniques are based on restoring image values based on local smoothness constraints within fixed bandwidth windows where image structure is not considered. Common problem for such methods is how to choose the most appropriate bandwidth and the most suitable set of neighboring pixels to guide the reconstruction process. The present work proposes a denoising technique based on particle filtering using MRF (Markov Random Field). It is an automatic technique to capture the scale of texture. The contribution of our method is the selection of an appropriate window in the image domain. For this we first construct a set containing all occurrences then the conditional pdf can be estimated with a histogram of all center pixel values. Particle evolution is controlled by the image structure leading to a filtering window adapted to the image content. Our method explores multiple neighbors’ sets (or hypotheses) that can be used for pixel denoising, through a particle filtering approach. This technique associates weights for each hypothesis according to its relevance and its contribution in the denoising process.
This document summarizes a research paper that compares different image filtering methods for reducing noise, including an adaptive bilateral filter, median filter, and Butterworth filter. The paper applies these filters to images with added Gaussian white noise and compares the results based on visual quality, mean squared error (MSE), and peak signal-to-noise ratio (PSNR). It finds that the adaptive bilateral filter produces the best results with the lowest MSE and highest PSNR, indicating it most effectively removes noise while preserving image details and sharpness.
The document proposes a hybrid approach for segmenting brain tumors in MRI images using wavelet and watershed transforms. It begins with applying wavelet transform to produce approximation and detail images for noise reduction. Edge detection is then performed on the approximation image. Watershed transform is applied for initial segmentation at low resolution. Repeated inverse wavelet transform is used to increase the segmented image resolution. Region merging is applied for further segmentation refinement before cropping the tumor area. The results show this coactive wavelet-watershed approach can help achieve accurate tumor segmentation.
Correction of Inhomogeneous MR Images Using Multiscale RetinexCSCJournals
A new method for enhancing the contrast of magnetic resonance images (MRI) by retinex algorithm is proposed. It can correct the blurrings in deep anatomical structures and inhomogeneity of MRI. Multiscale retinex (MSR) employed SSR with different weightings to correct inhomogeneities and enhance the contrast of MR images. The method was assessed by applying it to phantom and animal images acquired on MRI scanner systems. Its performance was also compared with other methods based on two indices: (1) the peak signal-to-noise ratio (PSNR) and (2) the contrast-to-noise ratio (CNR). Two indices, including PSNR and CNR, were used to evaluate the performance of correction of inhomogeneity in MR images. The PSNR/CNR of a phantom and animal images were 11.8648 dB/2.0922 and 11.7580 dB/2.1157, respectively, which were higher or very close to the results of wavelet algorithm. The retinex algorithm successfully corrected a nonuniform grayscale, enhanced contrast, corrected inhomogeneity, and clarified the deep brain structures of MR images captured by surface coils and outperformed histogram equalization, local histogram equalization, and a waveletbased algorithm, and hence may be a valuable method in MR image processing.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
Image Denoising is an important part of diverse image processing and computer vision problems. The
important property of a good image denoising model is that it should completely remove noise as far as
possible as well as preserve edges. One of the most powerful and perspective approaches in this area is
image denoising using discrete wavelet transform (DWT). In this paper, comparison of various Wavelets at
different decomposition levels has been done. As number of levels increased, Peak Signal to Noise Ratio
(PSNR) of image gets decreased whereas Mean Absolute Error (MAE) and Mean Square Error (MSE) get
increased . A comparison of filters and various wavelet based methods has also been carried out to denoise
the image. The simulation results reveal that wavelet based Bayes shrinkage method outperforms other
methods.
Airtel leveraged its sponsorship of the Champions League T20 cricket tournament in India to engage fans on YouTube. It held a contest asking fans to make and upload cheering videos for their favorite teams, with the top 10 most viewed videos winning a trip to the tournament finals in South Africa. The campaign was a success, receiving over 2,500 video submissions and making the Airtel YouTube channel the most subscribed in India that month. It established Airtel as supporting fans' passion and talent while generating engagement through social media.
This document discusses a Facebook advertising campaign for Manchester United Café Bar. It mentions creating a sponsored ad and Facebook page to promote a live event featuring Ronaldo versus Rooney at the café bar. The document thanks the reader, suggesting help was provided for the small business's Facebook marketing.
Kiehl's presentation - The Three Kiehler'sJohann Fromont
This document outlines a pre-case study for Kiehl's branding strategy in the men's skincare market. It begins with an analysis of market trends, competitors, Kiehl's target consumer, and a SWOT analysis. Opportunities for Kiehl's include expanding in the men's market and emerging markets. The proposed product is a Facial Fuel Energizing Tonic for Men. The marketing strategy discusses the product, place, price and promotion approaches, focusing on a social media campaign around a "Kiehl's Epic Journey" and developing an informational app.
The document summarizes the Nano Drive campaign by MTV in India. It was India's first 21-day, 2000km social road trip with 4 teams traveling from different regions in Tata Nanos. Each team was led by an MTV VJ and had a daily budget of Rs. 4000 for food, accommodation, and petrol. The teams documented their journeys on social media platforms for audiences to follow along. The campaign was a unique way to test social engagement through an interactive road trip competition. It performed very well online, gaining over 141,000 Facebook fans and generating hundreds of thousands of tweets and YouTube videos over the 21 days.
Kiehl's India launched a Twitter campaign to promote the opening of their new Mumbai store, running daily and weekly contests to engage the local community and create brand awareness. The campaign actively engaged with health and beauty influencers and relevant conversations in the community to promote contests and keep people interested, with the goal of engaging the right audiences for the new store launch.
Facebook Advertising: A Champagne Campaign on a Beer BudgetJoseph LaMountain
Facebook ads are hard to miss. They’re on your newsfeed, in the sidebar, on your mobile device, and on your logout screen. Yet few companies, nonprofits, or government agencies use Facebook advertising as part of their social media strategy. The good news is that you don’t need a six-figure budget to run an effective Facebook ad campaign.
Facebook’s “social ads” take advantage of social context by highlighting friends’ interactions with content. Social context paired with advanced targeting capabilities allow savvy marketers the opportunity to push content directly to the Facebook users they want to reach. This increases ad effectiveness and engagement while it dramatically reduces the cost of your ad buy.
In this session, we’ll discuss and show how to identify and reach your target audiences, test message effectiveness, analyze performance, and optimize your results. Speakers include Reingold Partner Jack Benson, Senior Social Media Associate Erin Fenn, and Senior Digital Marketing Associate Brooks Lape.
Placing ads on Facebook provides one of the most targeted advertising opportunities today for reaching targeted audience globally.. http://www.toboc.com
Bases de datos distribuidas y bases de datos clienteGerardo
El documento describe las bases de datos distribuidas y las bases de datos cliente-servidor. Una base de datos distribuida consiste en múltiples bases de datos lógicamente relacionadas que se distribuyen en diferentes servidores y sitios. Un sistema de bases de datos distribuida permite el procesamiento autónomo y acceso a los datos desde cualquier lugar de la red. Una base de datos cliente-servidor separa las tareas entre servidores que almacenan y gestionan los datos y clientes que hacen solicitudes a los servidores. La mayoría de los servicios de Internet como
Ads on Facebook are unique. They're shown to specific groups of highly engaged people on desktop and mobile. When your ads have great creative content and are well targeted, they get more likes, comments and shares. When someone takes any of these actions, their friends may see your ad, making it more powerful. The more you promote your posts and create targeted ads to specific groups of people, the more likely they are to see it when they visit Facebook.
This document provides an overview and analysis of the men's grooming market and Kiehl's brand. It finds that the global men's grooming market is worth $7.58 billion and grew 31.8% from 2007-2012, while the UK market grew 5% to £1.1 billion in 2012. Kiehl's targets this market with premium-priced men's skincare and hair products sold through selective distribution globally and in the UK. The document analyzes Kiehl's marketing strategy, competitors, target customers, and the macro-environment.
Kiehl's Case Study - Company & Industry Analysis vis-a-vis Men's Grooming Nikhil Saraf
Analyzed the status of Kiehl’s:
- SWOT analysis of the Kiehl’s brand when it comes to male beauty products
- Business performance
- Products (packaging, visuals, ranges, geography, claims, USP <unique>)
- Market share and ranking
- Positioning and image
- Pricing
- Consumers
- PR & Digital strategy
- Retail strategy
- Merchandising
- In-store & online activities
[Entry for Loreal Brandstorm 2014]
Sistema para el control de ventas e inventariosAidil Sanchez
Este documento presenta una tesis profesional para obtener el título de Licenciado en Sistemas Computacionales. El trabajo consiste en el desarrollo de un sistema para el control de ventas e inventarios de la empresa Antiguo Arte Europeo S.A. de C.V. El sistema se desarrollará utilizando el lenguaje de programación Visual Basic 6.0 y una base de datos relacional. El documento incluye el marco teórico, análisis y diseño del sistema, desarrollo del código y pruebas realizadas.
Abstract: These days analysing patient data in the form of medical images to perform diagnose while doing detection
and prediction of a disease has emerged as a biggest research challenge. All these medical images can be in the form of
X-RAY, CT scan, MRI, PET and SPECT. These images carry minute information about heart, brain, nerves etc within
themselves. It may happen that these images get corrupted due to noise while capturing them. This makes the complete
image interpretation process very difficult and inaccurate. It has been found that the accuracy rate of existing method is
very less so improvement is required to make them more accurate. This paper proposes a Machine Learning Model based
on Convolutional Neural Network (CNN) that will contain all the filters required to de-noise MRI or USI Images. This
model will have same error rate efficiency like those of data mining techniques which radiologists were interested in. The
filters used in the proposed work are namely Weiner Filter, Gaussian Filter, Median Filter that are capable of removing
most common noises such as Salt and Pepper, Poisson, Speckle, Blurred, Gaussian existing in MRI images in Grey Scale
and RGB Scale.
Keywords: Convolution Neural Network, Denoising, Machine Learning, Deep Learning, Image Noise, Filters
Performance analysis of image filtering algorithms for mri imageseSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
Performance analysis of image filtering algorithms for mri imageseSAT Publishing House
This document analyzes the performance of three image filtering algorithms (median filter, Wiener filter, and center weighted median filter) at removing noise from MRI images. The algorithms are tested on MRI images corrupted with different noise types. The Wiener filter is found to reconstruct images with the highest quality according to measurements of mean square error and peak signal-to-noise ratio. The study concludes the Wiener filter provides the best denoising of MRI images compared to the other algorithms tested.
The document compares several modern denoising algorithms for removing salt and pepper noise from images: the median filter, tolerance-based selective arithmetic mean filter technique (TSAMFT), and improved tolerance-based selective arithmetic mean filter technique (ITSAMFT) in 1 or 2 levels. It presents experimental results on the Lena test image corrupted with salt and pepper noise levels from 50% to 95%. The results show that Level-2 ITSAMFT performs best in maintaining high peak signal-to-noise ratio, correlation, image enhancement factor, and is most powerful at removing heavy salt and pepper noise, even at noise densities above 50% where other techniques begin to degrade.
The document discusses various techniques for removing speckle noise from images, which is a type of noise that inherently exists in synthetic aperture radar (SAR) images. It describes common speckle noise removal methods like median filters, Wiener filters, Frost filters, and Lee filters. The document concludes that the Wiener filter is generally best for removing speckle noise as it minimizes the mean square error when filtering.
Ultrasound medical images are very important component of the diagnostics process.
As a part of image analysis, edge detection is often considered for further segmentation
or more precise measurements of patterns in the picture. Unfortunately, ultrasound
images are subject to degradations, especially speckle noise which is also a high
frequency component. Conventional edge detector can detect edges in image with additive
noise effectively but not ultrasound image that are corrupted by multiplicative speckle
noise which alleviates image resolution resulting in inaccurate characterization of object
features. In this paper, anisotropic diffusion and PSO-EM based edge detectors are
analyzed and compared for the suppression of the multiplicative noise effectively while
preserving the edge of the object in ultrasound image. The result shows that the proposed
methods provided better result than conventional method
International Journal of Computational Engineering Research(IJCER)ijceronline
International Journal of Computational Engineering Research(IJCER) is an intentional online Journal in English monthly publishing journal. This Journal publish original research work that contributes significantly to further the scientific knowledge in engineering and Technology.
Pattern Approximation Based Generalized Image Noise Reduction Using Adaptive ...IJECEIAES
The problem of noise interference with the image always occurs irrespective of whatever precaution is taken. Challenging issues with noise reduction are diversity of characteristics involved with source of noise and in result; it is difficult to develop a universal solution. This paper has proposed neural network based generalize solution of noise reduction by mapping the problem as pattern approximation. Considering the statistical relationship among local region pixels in the noise free image as normal patterns, feedforward neural network is applied to acquire the knowledge available within such patterns. Adaptiveness is applied in the slope of transfer function to improve the learning process. Acquired normal patterns knowledge is utilized to reduce the level of different type of noise available within an image by recorrection of noisy patterns through pattern approximation. The proposed restoration method does not need any estimation of noise model characteristics available in the image not only that it can reduce the mixer of different types of noise efficiently. The proposed method has high processing speed along with simplicity in design. Restoration of gray scale image as well as color image has done, which has suffered from different types of noise like, Gaussian noise, salt &peper, speckle noise and mixer of it.
New Noise Reduction Technique for Medical Ultrasound Imaging using Gabor Filt...CSCJournals
Ultrasound (US) imaging is an important medical diagnostic method, as it allows the examination of several internal body organs. However, its usefulness is diminished by signal dependent noise known as speckle noise. Speckle noise degrades target detectability in ultrasound images and reduces contrast and resolution, affecting the ability to identify normal and pathological tissue. For accurate diagnosis, it is important to remove this noise from ultrasound images. In this study, a new filtering technique is proposed for removing speckle noise from medical ultrasound images. It is based on Gabor filtering. Specifically, a preprocessing step is added before applying the Gabor filter. The proposed technique is applied to various ultrasound images, and certain measurement indexes are calculated, such as signal to noise ratio, peak signal to noise ratio, structure similarity index, and root mean square error, which are used for comparison. In particular, five widely used image enhancement techniques were applied to three types of ultrasound images (kidney, abdomen and ortho). The main objective of image enhancement is to obtain a highly detailed image, and in that respect, the proposed technique proved superior to other widely used filters.
AN ADAPTIVE THRESHOLD SEGMENTATION FOR DETECTION OF NUCLEI IN CERVICAL CELLS ...cscpconf
PAP smear test is the most efficient and easy procedure to detect any abnormality in cervical cells. It becomes difficult for the cytologist to analyse a large set of PAP smear test images
when there is a rapid increase in the incidence of cervical cancer. On the replacement, image analysis could swap manual interpretation. This paper proposes a method for the detection of cervical cells in pap smear images using wavelet based thresholding. First, Wiener filter is used for smoothing to suppress the noise and to improve the contrast of the image. Second, optimal threshold is been obtained for segmenting the cell by various Wavelet shrinkage techniques like VisuShrink, BayesShrink and SureShrink thresholding which segment the foreground from the background and detect cell component like nucleus from the clustered cell images. From the
results, it is proved that the performance of the adaptive Wiener filter with combination of SureShrink thresholding performs better in terms of threshold values and Mean Squared Error
than the other comparative methods. The succeeding research work can be carried out based on the size of the segmented nucleus which therefore helps in differentiating abnormality among the cells.
Advance in Image and Audio Restoration and their Assessments: A ReviewIJCSES Journal
Image restoration is the process of restoring the original image from a degraded one. Images can be affected by various types of noise, such as Gaussian noise, impulse noise, and affected by blurring, which is happened during image recordings like motion blur, Out-of-Focus Blur, and others. Image restoration techniques are used to reverse the effect of noise and blurring. Restoration of distorted images can be done using some information about noise and the blurring nature or without any knowledge about the image degradation process. Researchers have proposed many algorithms in this regard; in this paper, different noise and degradation models and restoration methods will be discussed and review some researches in this field.
Performance Comparison of Various Filters and Wavelet Transform for Image De-...IOSR Journals
This document compares different filtering and wavelet transform approaches for image de-noising. It adds three types of noise (Gaussian, salt and pepper, speckle) to an image and uses median, Wiener, Gaussian, average filters and wavelet transform to remove the noise. It evaluates the performance of each approach using peak signal-to-noise ratio and root mean square error. The results show that wavelet transform performs best for removing Gaussian and speckle noise, while median filtering works best for salt and pepper noise removal. Overall, wavelet transform is concluded to be very effective for de-noising all types of noise.
This document summarizes a study on automatically detecting boundaries and regions of interest in ultrasound images of focal liver lesions. The researchers used texture analysis and gradient vector flow snakes to extract boundaries after applying speckle noise reduction filters. They compared median, mean, Wiener and Gaussian low-pass filters, finding median filtering worked best with a PSNR of 31.3089dB. Gray-level co-occurrence matrix texture analysis, morphological operations and seed point determination were then used to generate regions of interest. The automatic method effectively detected lesion boundaries and regions of interest in ultrasound images.
This document summarizes a study on automatically detecting boundaries and regions of interest in ultrasound images of focal liver lesions. The researchers used texture analysis and gradient vector flow snakes to extract boundaries after reducing speckle noise. They tested several noise filters and found median filtering worked best, achieving the highest PSNR. Texture analysis via gray-level co-occurrence matrix extraction detected regions more accurately than range or standard deviation filters. Morphological operations and seed point determination were then used to generate the final region of interest. The proposed automatic method facilitates ultrasound image segmentation and analysis of focal liver lesions.
IRJET- A Review on Various Restoration Techniques in Digital Image ProcessingIRJET Journal
The document reviews various image restoration techniques used for removing noise and blurring from digital images. It discusses techniques like median filtering, Wiener filtering, and Lucy Richardson algorithms. It provides an overview of each technique, including their advantages and limitations. The document also reviews several research papers that propose modifications to existing techniques or new methods for tasks like salt-and-pepper noise removal. The reviewed papers found that their proposed methods improved restoration quality over other techniques, achieving higher PSNR values and producing images that looked visually sharper and more distinct.
Analysis of Various Image De-Noising Techniques: A Perspective Viewijtsrd
A critical issue in the image restoration is the problem of de noising images while keeping the integrity of relevant image information. A large number of image de noising techniques are proposed to remove noise. Mainly these techniques are depends upon the type of noise present in images. So image de noising still remains an important challenge for researchers because de noising techniques remove noise from images but also introduces some artifacts and cause blurring. In this paper we discuss about various image de noising and their features. Some of these techniques provide satisfactory results in noise removal and also preserving edges with fine details present in images. Noise modeling in images is greatly affected by capturing instruments, data transmission media, image quantization and discrete sources of radiation. Different algorithms are used depending on the noise model. Most of the natural images are assumed to have additive random noise which is modeled as a Gaussian. Speckle noise is observed in ultrasound images whereas Rician noise affects MRI images. The scope of the paper is to focus on noise removal techniques for natural images. Bhavna Kubde | Prof. Seema Shukla "Analysis of Various Image De-Noising Techniques: A Perspective View" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-1 , December 2019, URL: https://www.ijtsrd.com/papers/ijtsrd29629.pdfPaper URL: https://www.ijtsrd.com/computer-science/other/29629/analysis-of-various-image-de-noising-techniques-a-perspective-view/bhavna-kubde
Comparison of Denoising Filters on Greyscale TEM Image for Different NoiseIOSR Journals
This document compares five different filters for removing noise from transmission electron microscopy (TEM) images: Wiener filter using discrete wavelet transform, hybrid median filter, bilateral filter, dual vectorial ROF filter, and fuzzy histogram equalization. Four types of noise are added to TEM images at varying levels: Gaussian noise, speckle noise, salt and pepper noise, and Poisson noise. The filters are applied to the noisy images and evaluated based on mean, mean square error, signal-to-noise ratio, and peak signal-to-noise ratio. Simulation results show that the dual vectorial ROF filter performs the best according to the evaluation metrics for each type of noise.
This document summarizes a student's analysis of different image filtering techniques to reduce noise. It outlines the objective to compare mean, median, adaptive median, and bilateral filters. It introduces various types of image noise like salt and pepper, Gaussian, and speckle noise. Performance is analyzed using PSNR scores. Adaptive median filtering achieved the best results for salt and pepper noise below 0.5 density and Gaussian noise. Average filtering worked best for speckle noise, but frequency domain filters are needed to significantly reduce speckle noise. PSNR is limited and SSIM would provide a better quality assessment.
Noise Reduction Technique using Bilateral Based FilterIRJET Journal
This document proposes a noise removal technique using a bilateral filter based on spatial gradients and minimum mean square error (MMSE) filtering. It consists of two steps: 1) A reference image is generated from the noisy image by applying a spatial gradient-based bilateral filter to 3x3 patches, and 2) An MMSE filter is applied to the reference image to reduce the mean square error. Generally, noise removal techniques alter the natural appearance of images, but this method aims to restore the image without affecting its natural appearance. The technique is evaluated using metrics like PSNR to compare its performance to other filters.
PERFORMANCE ANALYSIS OF UNSYMMETRICAL TRIMMED MEDIAN AS DETECTOR ON IMAGE NOI...ijistjournal
This Paper Analyze the performance of Unsymmetrical trimmed median, which is used as detector for the detection of impulse noise, Gaussian noise and mixed noise is proposed. The proposed algorithm uses a fixed 3x3 window for the increasing noise densities. The pixels in the current window are arranged in sorting order using a improved snake like sorting algorithm with reduced comparator. The processed pixel is checked for the occurrence of outliers, if the absolute difference between processed pixels is greater than fixed threshold. Under high noise densities the processed pixel is also noisy hence the median is checked using the above procedure. if found true then the pixel is considered as noisy hence the corrupted pixel is replaced by the median of the current processing window. If median is also noisy then replace the corrupted pixel with unsymmetrical trimmed median else if the pixel is termed uncorrupted and left unaltered. The proposed algorithm (PA) is tested on varying detail images for various noises. The proposed algorithm effectively removes the high density fixed value impulse noise, low density random valued impulse noise, low density Gaussian noise and lower proportion of mixed noise. The proposed algorithm is targeted on Xc3e5000-5fg900 FPGA using Xilinx 7.1 compiler version which requires less number of slices, optimum speed and low power when compared to the other median finding architectures.
PERFORMANCE ANALYSIS OF UNSYMMETRICAL TRIMMED MEDIAN AS DETECTOR ON IMAGE NOI...
Efficient analysis mri us 1
1. EFFICIENT ANALYSIS OF MEDICAL IMAGE DE-NOISING FOR MRI AND
ULTRASOUND IMAGES
Mohamed Saleh Abuazoum (Student: GE100006) and Afandi Ahmad (Supervisor)
Department of Computer Engineering
Faculty of Electrical and Electronic Engineering
Universiti Tun Hussein Onn Malaysia, Johor, Malaysia
mhmd_abu76@yahoo.com
ABSTRACT free of noise and artefacts. While the technologies for
acquiring digital medical images continue to improve,
Magnetic resonance imaging (MRI) and ultrasound resulting in images of higher and higher resolution and
images have been widely exploited for more truthful quality, removing noise in these digital images remains
pathological changes as well as diagnosis. However, one of the major challenges in the study of medical
they suffer from a number of shortcomings and these imaging, because they could mask and blur important
includes: acquisition noise from the equipment, ambient subtle features in the images, many proposed de-noising
noise from the environment, the presence of techniques have their own problems. Image de-noising
background tissue, other organs and anatomical still remains a challenge for researchers because noise
influences such as body fat, and breathing motion. removal introduces artefacts and causes blurring of the
Therefore, noise reduction is very important, as various images [1].
types of noise generated limits the effectiveness of This project describes different methodologies for
medical image diagnosis. In this study, an efficient de-noising giving an insight as to which algorithm
analysis of MRI and ultrasound modalities have should be used to find the most reliable estimate of the
investigated. Three experiments have carried out that original image data given its degraded version. Noise
include various filters (Median, Gaussian and Wiener modelling in medical images is greatly affected by
filter). To validate the outcomes of medical image de- capturing instruments, data transmission media, image
noising, both objective (quantitative) and subjective quantization and discrete sources of radiation. Most of
(qualitative) tests will be used. Since the noise levels images are assumed to have additive random noise
are scanner-dependent, this study has underlined several which is modelled as a white Gaussian noise, therefore
significant parameters to be considered in a generic de- it is required to exterminate a variety of types of de-
noising algorithm for MRI and ultrasound modalities. noising algorithm for MRI and ultrasound modalities.
Keywords
Median filter, Gaussian filter, Wiener filter, peak signal-to- 1.2 Summary of Previous Researches
noise ratio (PSNR).
Table 1: Summary of Previous Research
1 INTRODUCTION Images Algorithms References
1.1 Background Ultrasound Wavelet Thresholding [2]
Image Novel Bayesian Multiscale [3]
The arrival of digital medical imaging technologies
such as positron emission tomography (PET), magnetic Magnetic Nonlinear anisotropic [4]
resonance imaging (MRI), computerized tomography resonance
(CT) and ultrasound Imaging has revolutionized image (MRI) wavelet transforms [5]
modern medicine. Today, many patients no longer need
to go through invasive and often dangerous procedures
multi-dimensional adaptive [6]
to diagnose a wide variety of illnesses. With the Computed
filter
widespread use of digital imaging in medicine today, tomography Edge-preserving Adaptive
the quality of digital medical images becomes an (CT) Filters [7]
important issue. To achieve the best possible diagnosis
it is important that medical images be sharp, clear, and
2. 1.3 Mathematical Background coefficient, as well as the size of the kernel used
(squares with an odd number of pixels; e.g. 3×3, 5×5
1.3.1 Median Filter
pixels, so that the pixel being acted upon is in the
The median filter is a popular nonlinear digital
middle). The processing can be speeded up by
filtering technique, often used to remove noise. Such
implementing the filtering in the frequency rather than
noise reduction is a typical pre-processing step to
spatial domain, especially for the slower convolution
improve the results of later processing (for example,
operation (which is implemented as the faster
edge detection on an image). Median filtering is very
multiplication operation in the frequency domain).
widely used in digital image processing because under
certain conditions, it preserves edges while removing 1.3.3 Wiener filter
noise. Which has been shown to be good at removing Wiener filters are a class of optimum linear filters
salt-and-pepper noise in images [8]. Sometimes known which involve linear estimation of a desired signal
as a rank filter, this spatial filter suppresses isolated sequence from another related sequence. It is not an
noise by replacing each pixel’s intensity by the median adaptive filter. The wiener filter’s main purpose is to
of the intensities of the pixels in its neighbourhood. It is reduce the amount of noise present in a image by
widely used in de-noising and image smoothing comparison with an estimation of the desired noiseless
applications. Median filters exhibit edge-preserving image. The Wiener filter may also be used for
characteristics (cf. linear methods such as average smoothing. This filter is the mean squares error-optimal
filtering tends to blur edges), which is very desirable for stationary linear filter for images degraded by additive
many image processing applications as edges contain noise and blurring. It is usually applied in the frequency
important information for segmenting, labelling and domain (by taking the Fourier transform) [10], due to
preserving detail in images. This filter may be linear motion or unfocussed optics Wiener filter is the
represented by (1). most important technique for removal of blur in images.
From a signal processing standpoint. Each pixel in a
digital representation of the photograph should
represent the intensity of a single stationary point in
where front of the camera. Unfortunately, if the shutter speed
Filter window with pixel as its is too slow and the camera is in motion, a given pixel
middle will be an amalgram of intensities from points along the
line of the camera's motion.
The goal of the Wiener filter is to filter out noise
1.3.2 Gaussian filter that has corrupted a signal. It is based on a statistical
Gaussian filter is a filter whose impulse response is
approach. Typical filters are designed for a desired
a Gaussian function. Gaussian filters are designed to
frequency response. The Wiener filter approaches
give no overshoot to a step function input while
filtering from a different angle. One is assumed to have
minimizing the rise and fall time. This behaviour is
knowledge of the spectral properties of the original
closely connected to the fact that the Gaussian filter has
signal and the noise, and one seeks the LTI filter whose
the minimum possible group delay. Mathematically, a
output would come as close to the original signal as
Gaussian filter modifies the input signal by convolution
possible [11]. Wiener filters are characterized by the
with a Gaussian function; this transformation is also
following:
known as the Weierstrass transform. Smoothing is
1. Assumption: signal and (additive) noise are
commonly undertaken using linear filters such as the
stationary linear random processes with known spectral
Gaussian function (the kernel is based on the normal
characteristics.
distribution curve), which tends to produce good results
2. Requirement: the filter must be physically realizable,
in reducing the influence of noise with respect to the
i.e. causal (this requirement can be dropped, resulting in
image. The 1D and 2D Gaussian distributions with
a non-causal solution).
standard deviation for a data point (x) and pixel (x,y),
3. Performance criteria: minimum mean-square error.
are given by (2) and (3), respectively [9].
Wiener Filter in the Fourier Domain as in (4).
Where
= Fourier transform of the point spread
The kernel could be extended to further dimensions function
as well. For an image, the 2D Gaussian distribution is = Power spectrum of the signal process,
used to provide a point-spread; i.e. blurring over obtained by taking the Fourier transform of the signal
neighbouring pixels. This is implemented on every autocorrelation
pixel in the image using the convolution operation. The
degree of blurring is controlled by the sigma or blurring
3. = Power spectrum of the noise process, 2 RESEARCH METHODOLOGY
obtained by taking the Fourier transform of the noise
autocorrelation
It should be noted that there are some known limitations 2.1 The concept of de-noising filters
to Wiener filters. They are able to suppress frequency
components that have been degraded by noise but do The idea of every de-noising filter is different from
not reconstruct them. Wiener filters are also unable to other filters because every filter has its own function.
undo blurring caused by band limiting of , To give simple explanation of the de-noising filters
which is a phenomenon in real-world imaging systems. window 3 3 is used for median, Gaussian and Wiener
1.4 Peak Signal-to-Noise Ratio filter.
2.1.1 How does Median filter work?
The peak signal-to-noise ratio (PSNR) is the ratio The median filter considers each pixel in the image in
between a signal's maximum power and the power of turn and looks at its nearby neighbours to decide
the signal's noise. Engineers commonly use the PSNR whether or not it is representative of its surroundings.
to measure the quality of reconstructed images that have Instead of simply replacing the pixel value with the
been compressed. Each picture element (pixel) has a mean of neighbouring pixel values, it replaces it with
colour value that can change when an image is the median of those values [10].
compressed and then uncompressed. Signals can have a Median filter controls the pepper and Gaussian noises.
wide dynamic range, so PSNR is usually expressed in The median filter is reputed to be edge preserving. The
decibels, which is a logarithmic scale. transfer function used here is
The PSNR is most commonly used as a measure of
quality of reconstruction of lossy compression codecs
(e.g., for image compression). The signal in this case is
the original data, and the noise is the error introduced
by compression. When comparing compression codecs where is the intensity value in the middle
it is used as an approximation to human perception of position of the sorted array of the neighbouring pixels.
reconstruction quality, therefore in some cases one
reconstruction may appear to be closer to the original
than another, even though it has a lower PSNR (a
higher PSNR would normally indicate that the
reconstruction is of higher quality). One has to be
extremely careful with the range of validity of this
metric; it is only conclusively valid when it is used to Neighbourhood values are (0, 0, 0, 0, 0 , 4, 4, 12, 22)
compare results from the same codec (or codec type) Median value is 0
and same content [12]. It is most easily defined via
Figure 1: Sorting neighbourhood values and determine median value
the mean squared error (MSE), where it denotes the
mean square error for two m×n images I (i, j)& I (i, j)
where one of the images is considered a noisy The median is calculated by first sorting all the pixel
approximation of the other and is given by (5),(6). values from the surrounding neighbourhood into
numerical order and then replacing the pixel being
considered with the middle pixel value as was showed
in figure 1. If the neighbourhood under consideration
contains an even number of pixels, the average of the
The PSNR is defined by (7) two middle pixel values is used. The pattern of
neighbours is called the "window", which slides, pixel
by pixel over the entire image.
Where, MAXI is the maximum possible pixel value of
the image. When the pixels are represented using 8 bits
per sample, which is equal to 255. More generally,
when samples are represented using
linear PCM with B bits per sample, MAXI is 2B−1.
Typical values for the PSNR in lossy image and video
compression are between 30 and 50 dB, where higher is
better. Acceptable values for wireless transmission
quality loss are considered to be about 20 dB to 25 dB
[13]. Figure 2: Assumed pixels window represented on MRI
4. Median filtering using a 3x3 sampling window with the When working with images we need to use the two
extending border values outside with 0s dimensional Gaussian function Figure 7. This is
simply the product of two 1D Gaussian functions (one
for each direction) and is given by:
Figure 3: The movement of the window 3 3 (mask) on the pixels
Figure 7: Two dimensional Gaussians
The Gaussian filter works by using the 2D distribution
as a point-spread function. This is achieved by
convolving the 2D Gaussian distribution function with
the image. Before we can perform the convolution a
collection of discrete pixels we need to produce a
discrete approximation to the Gaussian function. In
Figure 4: Sorting the pixels and determining middle pixel value theory, the Gaussian distribution is non-zero
everywhere, which would require an infinitely large
convolution kernel, but in practice it is effectively zero
more than about three standard deviations from the
mean, and so we can truncate the kernel at this point.
The kernel coefficients diminish with increasing
distance from the kernel’s centre. Central pixels have a
higher weighting than those on the periphery. Larger
values of produce a wider peak (greater blurring).
Kernel size must increase with increasing to maintain
the Gaussian nature of the filter. Gaussian kernel
Figure 5: Pixels window after applying Median filter on all pixels coefficients depend on the value of . Figure 8 shows a
different convolution kernel that approximates a
2.1.2 How does Gaussian filter work? Gaussian with .
Gaussian filter are a class of low-pass filter,
all based on the Gaussian probability distribution
function used to blur images and remove noise and
detail. In one dimension, the Gaussian function is:
Figure 8: 3 3 windows with = 0.849, = 1, =2
Where is the standard deviation of the distribution
The distribution is assumed to have a mean of 0. Shown The idea of these windows distribution comes from
graphically, we see the familiar bell shaped Gaussian
distribution, where a large value of produces to a
flatter curve, and a small value leads to a
“pointier” curve. Figure 6 shows examples of
such one dimensional Gaussians [14].
Figure 9: 3×3 windows with σ = 0.849
Where & 16 is the summation of the
values in 3 3 window with this window is
Large value of Small value of
using to explain how Gaussian filter is working by
Figure 6: One dimensional gaussians convolute it on the pixels of MRI.
5. 2.1.3 How does Wiener filter work?
Wiener filters are a class of optimum linear filters
which involve linear estimation of a desired signal
sequence from another related sequence. In this filter
the same window of MRI pixels will be used to
illustrate how Wiener filter is working [15].
Figure 10: Assumed pixels window represented on MRI
Figure 14: Assumed pixels window represented on MRI
Figure 11: The movement of the window 3 x 3 (mask) on the pixels
To achieve the function of Gaussian filter, the 2D
Gaussian distribution of 3 3 window with
must be convolved on the first window of MRI
pixels to get first value of filtered pixel.
Figure 15: The movement of the window 3 x 3 (mask) on the pixels
By applying Wiener filter, the first window of all the
pixels Figure 16 will be calculated by estimating the
local mean and variance around each pixel to find the
value of the central window.
Figure 12: 3×3 windows with σ = 0.849, first window of MRI pixels
Figure 16: first window of the pixels
To find the value of the central window, by
calculating the equation (9)
Figure 13: Pixels window after applying Gaussian filter on all pixels
6. 2.2 PSNR Calculation
Define the mean squared error (MSE) between two
monochromatic images, where one image is considered
to be an approximation of the other. The MSE can be
described as the mean of the square of the differences in
the pixel values between the corresponding pixels of the
where is the N-by-M local neighborhood of each pixel two images [16].
in the image then creates a pixelwise Wiener filter using
these estimates, The MSE can be calculated using equation (12).
where is the noise variance. In this 3 3 window the where I and K are matrices that represent the images
noise variance is 30.9790 which is calculated by Matlab being compared. The two summations are performed
according to the pixels input. for the dimensions "i" and "j." Therefore I(i,j)
represents the value of pixel (i,j) of image I. Determine
the maximum possible value of the pixels in image I.
Typically, this may be given as ( ) - 1 where n is the
This is the value of the first pixel after Wiener filter number of bits that represent the pixel. Thus, an 8-bit
calculation, and then we move to the right one step to pixel would have a maximum value of ( ) - 1 = 255.
find the second pixel. Let the maximum value for pixels in image I be .
Express the PSNR in decibels.
2.3 Description of Methodology
We move to the right one step to find the Third pixel.
The description of working in order to reduce the noise
medical images (ultrasound image and MRI), is
illustrated by referring to image de-noising chart which
represented in Figure 18.
Figure 18: Image De-noising Chart
Figure 17: Pixels window after applying Wiener filter on all pixels
7. The methodology of this study requires collecting 4. Calculating PSNR for noisy MRI Image and de-
medical image data which include two types of noised MRI with every windows size from (1 1) to
modalities MRI and ultrasound image which are (6 6).
respectively in Figure 19 (a) and (b).
5. Comparing PSNR result of noisy MRI with every
PSNR result and determine whether we got better
PSNR or no, which is known by getting PSNR
result greater than PSNR result of noisy MRI.
2.4.2 Applying Median filter on Ultrasound
Image
1. Loading ultrasound image in the command window
of the matlab software to perform the needed
operations as shown in Figure 22.
(a) (b)
Figure 19: MRI Image (a) and Ultrasound Image (b)
2.4 Median filter implementation
2.4.1 Applying Median filter on MRI
1. Loading MRI image in the command window of
the matlab software to perform the needed
operations as shown in Figure 20.
Figure 22: Ultrasound Image
2. Generate White Gaussian Noise on the Ultrasound
Image to become noisy image as depicted in Figure
23.
Figure 20: MRI Image
2. Generate White Gaussian Noise on the MRI image
to become noisy image as depicted in Figure 21.
Figure 23: Noisy Ultrasound Image
3. Applying Median filter on the Ultrasound Image
with different parameters to determine the reliable
size of the window (kernel) which including the
sizes from (1 1) to (6 6).
4. Calculating PSNR for noisy Ultrasound Image and
de-noised Ultrasound Image with every windows
size from (1 1) to (6 6).
Figure 21: Noisy MRI Image
3. Applying Median filter on the MRI image with 5. Comparing PSNR result of noisy Ultrasound Image
different parameters to determine determine the with every PSNR result and determine whether we
reliable size of the window (kernel) which got better PSNR or no, which is known by getting
including the sizes from (1 1) to (6 6). PSNR result greater than PSNR result of noisy
Ultrasound.
8. 2.5 Gaussian filter implementation
2.5.1 Applying Gaussian filter on MRI
1. Loading MRI image in the command window of
the matlab software to perform the needed
operations as shown in Figure 24.
Figure 26: Ultrasound Image
2. Generate White Gaussian Noise on the MRI image
to become noisy image as depicted in Figure 27.
Figure 24: MRI Image
2. Generate White Gaussian Noise on the MRI image
to become noisy image as depicted in Figure 25.
Figure 27: Noisy Ultrasound Image
3. Applying Gaussian filter on the Ultrasound image
with different parameters to determine determine
the reliable size of the window (kernel) which
including the sizes from (1 1) to (6 6) and
Figure 25: Noisy MRI Image standard deviation .
3. Applying Gaussian filter on the MRI image with
different parameters to determine determine the 4. Calculating PSNR for noisy Ultrasound Image and
reliable size of the window (kernel) which de-noised Ultrasound Image with every windows
including the sizes from (1 1) to (6 6) and size from (1 1) to (6 6) with different standard
standard deviation . deviation .
5. Comparing PSNR result of noisy Ultrasound Image
4. Calculating PSNR for noisy MRI Image and de-
with every PSNR result and determine whether we
noised MRI with every windows size from (1 1) to
got better PSNR or no, which is known by getting
(6 6) with different standard deviation .
PSNR result greater than PSNR result of noisy
MRI.
5. Comparing PSNR result of noisy MRI with every
PSNR result and determine whether we got better
PSNR or no, which is known by getting PSNR
result greater than PSNR result of noisy MRI. 2.6 Wiener filter implementation
2.6.1 Applying Wiener filter on MRI
2.5.2 Applying Gaussian filter on Ultrasound
Image 1. Loading MRI image in the command window of
1. Loading ultrasound image in the command window the matlab software to perform the needed
of the matlab software to perform the needed operations as shown in Figure 28.
operations as shown in Figure 26.
9. 2. Generate White Gaussian Noise on the Ultrasound
Image to become noisy image as depicted in Figure
31.
Figure 28: MRI Image
2. Generate noise like White Gaussian Noise on the
MRI image to become noisy image as depicted in
Figure 29. Figure 31: Noisy Ultrasound Image
3. Applying Wiener filter on the Ultrasound Image
with different parameters to determine the reliable
size of the window (kernel) which including the
sizes from (1 1) to (6 6).
4. Calculating PSNR for noisy Ultrasound Image and
de-noised Ultrasound Image with every windows
size from (1 1) to (6 6).
5. Comparing PSNR result of noisy Ultrasound Image
Figure 29: Noisy MRI Image with every PSNR result and determine whether we
got better PSNR or no, which is known by getting
3. Applying Wiener filter on the MRI image with PSNR result greater than PSNR result of noisy
different parameters to determine determine the Ultrasound Image.
reliable size of the window (kernel) which
including the sizes from (1 1) to (6 6). 3 RESULTS AND ANALYSIS
4. Calculating PSNR for noisy MRI Image and de-
noised MRI with every windows size from (1 1) to
3.1 The result of Median filter on MRI and
(6 6).
ultrasound
5. Comparing PSNR result of noisy MRI with every
1. PSNR results of de-noised MRI by Median filter
PSNR result and determine whether we got better
from window size (1 1) to (6 6) which show this
PSNR or no, which is known by getting PSNR
window size (3 3) is the best with PSNR=14.01
result greater than PSNR result of noisy MRI.
Figure 32a, because it is greater than other PSNR
result and noisy MRI which is (9.19).
2.6.2 Applying Wiener filter on Ultrasound
Image
1. loading ultrasound image in the command window
of the matlab software to perform the needed
operations as shown in Figure 30.
(a) (b)
Figure 32: (a) PSNR results of de-noised MRI Image by Median
filter with windows size from (1 1) to (6 6), (b) De-noised MRI by
Median filter PSNR= 14.01
2. PSNR results of de-noised Ultrasound Image by
Figure 30: Ultrasound Image Median filter from window size (1 1) to (6 6)
10. which show this window size (3 3) is the best with 3.3 The result of Wiener filter on MRI and
PSNR=14.98 Figure 33a, because it is greater than ultrasound
other PSNR result and noisy Ultrasound Image 1. PSNR results of de-noised MRI by Wiener filter
which is (11.77). from window size (1 1) to (6 6) which show this
window size (3 3) is the best with PSNR=15.35
Figure 36a, because it is greater than other PSNR
result and noisy MRI which is (9.19).
(a) (b)
Figure 33: (a) PSNR results of de-noised ultrasound Image by
Median filter with windows size from (1 1) to (6 6), (b) De-noised
ultrasound Image by Median filter PSNR= 14.98.
(a) (b)
3.2 The result of Gaussian filter on MRI and Figure 36: (a) PSNR results of de-noised MRI Image by Wiener
ultrasound filter with windows size from (1 1) to (6 6), (b) De-noised MRI by
Wiener filter PSNR= 15.35
1. PSNR results of de-noised MRI by Gaussian filter
from window size (1 1) to (6 6) which show this 2. PSNR results of de-noised Ultrasound Image by
window size (3 3) is the best with PSNR=15.53 Wiener filter from window size (1 1) to (6 6)
with standard deviation Figure 34a, which show this window size (3 3) is the best
because it is greater than other PSNR result and with PSNR=17.27 Figure 37a, because it is greater
noisy MRI which is (9.19). than other PSNR result and noisy Ultrasound
Image which is (11.77).
(a) (b)
Figure 34: (a) PSNR results of de-noised MRI Image by Gaussian (a) (b)
filter with windows size from (1 1) to (6 6), , (b) De- Figure 37: (a) PSNR results of de-noised Ultrasound Image by
noised MRI by Median filter PSNR= 15.53 Wiener filter with windows size from (1 1) to (6 6), (b) De-noised
Ultrasound Image by Wiener filter PSNR= 17.27
2. PSNR results of de-noised Ultrasound Image by
Gaussian filter from window size (1 1) to (6 6)
which show this window size (3 3) is the best
with PSNR=17.37 Figure 35a, because it is greater Table 2: PSNR results of applying Median, Wiener and
Gaussian filter on 4 ultrasound images (US1,US2,US3) and 4
than other PSNR result and noisy Ultrasound MRI images (MRI1,MRI2,MRI3)
Image which is (11.77).
(a) (b)
Figure 35: (a) PSNR results of de-noised Ultrasound Image by
Gaussian filter with windows size from (1 1) to (6 6), ,
(b) De-noised Ultrasound Image by Gaussian filter PSNR= 17.37
11. By referring to Table 2 and comparing PSNR results
of noisy images with PSNR results of de-noised
images, also by looking to Figure 38 and comparing
noisy MRI (b1) with de-noised MRI by Median,
wiener and Gaussian filter (c1), (d1) and (e1) it can
observe that the noise in the de-noised MRI is
reduced and the image become better, then
comparing noisy ultrasound image (b2) with de-
noised ultrasound image by Median, wiener and
Gaussian filter (c2),(d2) and (e2) it can observe that
(a1) (a2) the noise in the de-noised ultrasound image is
reduced and the image become better.
4 CONCLUSIONS
This study shows one of the medical image de-noising
techniques by applying three filters (Medan, Gaussian
and Wiener filter) on the two modalities (MRI and
ultrasound Image). Consequently, after calculating and
comparing Peak Signal to Noise Ratio (PSNR) values
(b1) (b2)
between Noisy image with every De-noised image .It
can be observed that Gaussian filter is reliable for both
of images MRI and ultrasound image.
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