scholarly journals Comparative Image Quality Analysis of Spatial Filters for Pre-processing of CT Abdominal Images

Webology ◽  
2021 ◽  
Vol 18 (Special Issue 04) ◽  
pp. 1449-1469
Author(s):  
Ramya Mohan ◽  
S.P. Chokkalingam ◽  
Kirupa Ganapathy ◽  
A. Rama
Keyword(s):  
Image Processing ◽  
Gaussian Noise ◽  
Median Filter ◽  
Wiener Filter ◽  
Speckle Noise ◽  
Ct Images ◽  
Spatial Filters ◽  
Abdominal Ct ◽  
Wiener Filters ◽  
Salt And Pepper

Aim: To determine the efficient noise reduction filter for abdominal CT images. Background: Image enrichment is the first and foremost step that has to be done in all image processing applications. It is used to enhance the quality of digital images. Digital images are liable to addition of noise from various sources such as error in instrument calibration, excess staining of images, etc., Image de-noising is an enhancement technique used to remove / reduce noise present in an image. Reducing the noise of images and preserving its edges are always critical and challenging in image processing. Materials and Method: In this paper, four different spatial filters namely Mean, Median, Gaussian and Wiener were used on 100 CT abdominal images and their performances were compared against the following four parameters: Peak signal to noise ratio (PSNR), Mean Square Error (MSE), Normalised correlation coefficient (NCC) and Normalised Absolute Error (NAE) to determine the best denoising filter for the abdominal CT images. Result: Based on the experimental parameters, the median filter had the maximum efficiency in managing salt and pepper noise than the other three filters. Both Median and Wiener filters showed efficiency in removing the Gaussian noise. Whereas, the Wiener filter demonstrated higher efficiency in reducing both Poisson and Speckle noise. Conclusion: Based on the results of this study, we can conclude that the median filter can be used to reduce Salt and Pepper noises. Median and Wiener filters are significantly better for Gaussian Noise and the Wiener filter can be used to reduce both Poisson & Speckle noise in abdominal CT images.

scholarly journals Removal of various Noises in Dental X-ray Images using Selective Median Filter

2019 ◽  
Vol 8 (12) ◽  
pp. 2550-2558
Keyword(s):  
Image Processing ◽  
Gaussian Noise ◽  
Median Filter ◽  
Medical Image Analysis ◽  
Wiener Filter ◽  
Speckle Noise ◽  
Linear Filters ◽  
Severe Problem ◽  
X Ray ◽  
Non Linear

Image processing plays major role to provide additional information in medical diagnosis. Input images contain picture information as well as noise information. Noise information is added with the images during signal acquisition stage or in the transmission of image data. Salt & Pepper noise, Gaussian noise and Speckle noise is the major noises introduced in the images. Noise information may be interpreted as data and it may lead to severe problem. Linear and Non-linear filters are used to reduce these noises in the images. In medical image analysis, non-linear filters are preferred over linear filters because it preserves edge information. Dental X-ray image is used to identify the cavities and its depth. Average filter, median filter and wiener filter are the classical techniques used in many image processing applications. In this paper, three different noises (Salt &pepper, Gaussian and Speckle noise) are added and different filters (Average filters, median filter, Wiener filter) performances are analysed with the PSNR, SNR and MSE. Analysis shows that median filter is suitable for reducing salt & pepper noise and wiener filter is suitable for reducing Gaussian noise and speckle noise in the dental x-ray images. Selective median filter is a modified wiener filter. Median filter is used for the pixel value 0 and 255.For other pixel values wiener filter is used. Selective median filter is giving better result than traditional techniques

scholarly journals Biomedical Image Denoising Based on Hybrid Optimization Algorithm and Sequential Filters

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
N Yousefi Moteghaed
Keyword(s):  
Signal To Noise Ratio ◽  
Hybrid Genetic Algorithm ◽  
Wiener Filter ◽  
Structural Similarity ◽  
Speckle Noise ◽  
Ct Images ◽  
Biomedical Image ◽  
Processing Step ◽  
Salt And Pepper ◽  
Medical Analysis

Abstract Background: Nowadays, image de-noising plays a very important role in medical analysis applications and pre-processing step. Many filters were designed for image processing, assuming a specific noise distribution, so the images which are acquired by different medical imaging modalities must be out of the noise. Objectives: This study has focused on the sequence filters which are selected by a hybrid genetic algorithm and particle swarm optimization. Material and Methods: In this analytical study, we have applied the composite of different types of noise such as salt and pepper noise, speckle noise and Gaussian noise to images to make them noisy. The Median, Max and Min filters, Gaussian filter, Average filter, Unsharp filter, Wiener filter, Log filter and Sigma filter, are the nine filters that were used in this study for the denoising of medical images as digital imaging and communications in medicine (DICOM) format. Results: The model has been implemented on medical noisy images and the performances have been determined by the statistical analyses such as peak signal to noise ratio (PSNR), Root Mean Square error (RMSE) and Structural similarity (SSIM) index. The PSNR values were obtained between 59 to 63 and 63 to 65 for MRI and CT images. Also, the RMSE values were obtained between 36 to 47 and 12 to 20 for MRI and CT images. Conclusion: The proposed denoising algorithm showed the significantly increment of visual quality of the images and the statistical assessment.

Comparative Analysis on Noise Processing of Vacuum Switching Arc Images

2012 ◽  
Vol 229-231 ◽  
pp. 2615-2618
Author(s):  
Hua Jun Dong ◽  
Wen Liang Dong ◽  
Dao Shun Wang
Keyword(s):  
Image Processing ◽  
Digital Image Processing ◽  
Digital Image ◽  
Median Filter ◽  
Wiener Filter ◽  
Two Dimensions ◽  
Plasma Parameters ◽  
Arc Shape ◽  
Averaging Filter ◽  
Salt And Pepper

In order to diagnose the arc shape and the plasma parameters of vacuum switching arc effectively, it is necessary to do digital image processing on the arc images. The noises interfere greatly in the digital image processing of the arc, it makes images fuzzy and submerges the characteristics of the images. In this paper, based on the methods of average filter, two dimensions wavelet filter, wiener filter, several images averaging filter and median filter are used to filter the salt and pepper noised arc images. Combined with the results, we have analyzed and compared the effects of different noises filtering methods.

Reduction of Noise in Restoration of Images Using Mean and Median Filtering Techniques

2021 ◽  
Vol 9 (9) ◽  
pp. 301-313
Author(s):  
Vimal Chauhan
Keyword(s):  
Image Processing ◽  
Image Restoration ◽  
Median Filter ◽  
Processing Technique ◽  
Image Processing Technique ◽  
Digital Cameras ◽  
Salt And Pepper Noise ◽  
Satellite Imaging ◽  
Mean Filter ◽  
Salt And Pepper

Abstract: The purpose of this paper is to present a study of digital technology approaches to image restoration. This process of image restoration is crucial in many areas such as satellite imaging, astronomical image & medical imaging where degraded images need to be repaired Personal images captured by various digital cameras can easily be manipulated by a variety of dedicated image processing algorithms [2]. Image restoration can be described as an important part of image processing technique. Image restoration has proved to be an active field of research in the present days. The basic objective is to enhance the quality of an image by removing defects and make it look pleasing [2]. In this paper, an image restoration algorithm based on the mean and median calculation of a pixel has been implemented. We focused on a certain iterative process to carry out restoration. The algorithm has been tested on different images with different percentage of salt and pepper noise. The improved PSNR and MSE values has been obtained. Keywords: De-Noising, Image Filtering, Mean Filter & Median Filter, Salt and Pepper Noise, Denoising Techniques, Image Restoration.

Comparative Analysis on Noise Processing of Image

2013 ◽  
Vol 718-720 ◽  
pp. 2159-2162
Author(s):  
Hua Jun Dong ◽  
Xue Mei Jiang ◽  
Chen Xu Niu
Keyword(s):  
Image Processing ◽  
Comparative Analysis ◽  
Digital Image Processing ◽  
Digital Image ◽  
Median Filter ◽  
Wiener Filter ◽  
Image Noise ◽  
Two Dimensional ◽  
Wavelet Filter ◽  
Average Filter

The existence of noises have great interference on image processing, so the elimination of image noise is of great importance. In this paper, based on the digital image processing, the methods of average filter, wiener filter, median filter, two-dimensional wavelet filter, maximum and minimum filter are used to eliminate the salt & pepper noise of image. Then we analysis and compare the results of the five methods to find the best way to eliminate the image noise.

Gaussian Noise Filter Using Variable Step Time Matrix of PCNN

2011 ◽  
Vol 48-49 ◽  
pp. 551-554 ◽  
Author(s):  
Yuan Yuan Cheng ◽  
Hai Yan Li ◽  
Qi Xiao ◽  
Yu Feng Zhang ◽  
Xin Ling Shi
Keyword(s):  
Gaussian Noise ◽  
Spatial Information ◽  
Signal To Noise Ratio ◽  
Median Filter ◽  
Wiener Filter ◽  
Noise Filter ◽  
Variable Step ◽  
Mean Filter ◽  
Reduction Methods ◽  
Novel Method

A novel method was brought forward for the purpose of filtering Gaussian noise effectively by using variable step time matrix of the simplified pulse coupled neural network (PCNN). Firstly, the time matrix of PCNN, related to the grayscale and spatial information of an image, is calculated to identify the noise polluted pixels. Subsequently, a variable step, a long step for strong noise and a short step for weak noise, based on the time matrix is applied to modify the grayscale of noised pixels in a sliding window. And then wiener filter is used to the image to further filter the noise. Experiments show that the proposed filter can remove Gaussian noise effectively than other noise reduction methods such as median filter, mean filter, wiener filter etc, and the filtered image is smooth and the details and edges are sharp. Compared with existing PCNN based Gaussian noise filter, the proposed filter gets higher Peak Signal-to-Noise Ratio (PSNR) and better performance.

scholarly journals A DEEP LEARNING APPROACH FOR NOISY IMAGE CLASSIFICATION IN AUTOMATED DRIVER DROWSINESS DETECTION

2021 ◽  
Vol 25 (Special) ◽  
pp. 1-73-1-80
Author(s):  
Marwa K. Hussein ◽  
◽  
Tariq M. Salman ◽  
Abbas H. Miry ◽  
◽  
...  
Keyword(s):  
Detection System ◽  
Median Filter ◽  
Extended Period ◽  
Speckle Noise ◽  
Test Accuracy ◽  
Smart Systems ◽  
Noise Interference ◽  
Driver Drowsiness ◽  
Testing Accuracy ◽  
Salt And Pepper

In recent years, driver drowsiness has been a major cause of road accidents, particularly when the driver has been driving on the highway for an extended period of time. Smart systems can now be used to prevent accidents, and a reliable driver detection system must be applied to alert the driver. In these systems, several external factors have been degrading the performance of these systems, including added noise, interference and low illumination. To overcome these limitations, this paper presents a de-noising approach for noisy images; the results show that the enhanced images improve the overall system performance and classification accuracy. The final validation accuracy is 97.5%, while the testing accuracy for S1 is 96%, S2 is 92%, and S3 is 91%. The test accuracy of S1 decreased to 45% when the Salt and pepper noise is added to the set , , when Gaussian noise is added to S2 the testing accuracy decreased to 85%, and when speckle noise is added to S3 the testing accuracy is reduced to 73%. When the median filter is used the testing accuracy for S1 become 93%, the testing accuracy for S2 increase to 91%, and the testing accuracy for S3 raises to 85%.

scholarly journals Echocardiography image denoising using fractal wavelet transform

2018 ◽  
Vol 5 ◽  
pp. 23-33
Author(s):  
Reena Manandhar ◽  
Sanjeeb Prashad Pandey
Keyword(s):  
Image Processing ◽  
Wavelet Transform ◽  
Gaussian Noise ◽  
Medical Image ◽  
Signal To Noise Ratio ◽  
Wiener Filter ◽  
Hard Thresholding ◽  
Echocardiography Image ◽  
Different Types

One of the most important areas in image processing is medical image processing where the quality of the images has become an important issue. Most of the medical images are corrupted with the visual noise, and one of the such images is echocardiography image where this effect is more. So, this research aims to denoise the echocardiography image with fractal wavelet transform and to compare its performance with other wavelet based algorithm like hard thresholding, soft thresholding and wiener filter. Initially, the image is corrupted by the Gaussian noise with varying noise variances and is denoised using above mentioned different wavelet based denoising techniques. On comparison of the obtained results, it is observed that the fractal wavelet transform is well suited for highly degraded echocardiography images in terms of Mean Square Error (MSE) and Peak Signal To Noise Ratio (PSNR) than other wavelet based denoising methods. Further, the work could be enhanced to denoise the echocardiography image corrupted by other different types of noise. This research is limited to denoise the echocardiography image corrupted with Gaussian noise only.

MORPHOLOGICAL SEGMENTATION OF THE KIDNEYS FROM ABDOMINAL CT IMAGES

2014 ◽  
Vol 14 (05) ◽  
pp. 1450073 ◽  
Author(s):  
AICHA BELGHERBI ◽  
ISMAHEN HADJIDJ ◽  
ABDELHAFID BESSAID
Keyword(s):  
Computed Tomography ◽  
Image Processing ◽  
Ct Images ◽  
Second Step ◽  
Watershed Algorithm ◽  
Morphological Filters ◽  
Abdominal Ct ◽  
Morphological Segmentation ◽  
Kidney Segmentation ◽  
Anatomical Information

The phase of segmentation is an important step in the processing and interpretation of medical images. In this paper, we focus on the segmentation of kidneys from the abdomen computed tomography (CT) images. The importance of our study comes from the fact that the segmentation of kidneys from CT images is usually a difficult task. This difficulty is the gray's level which is similar to the spine level. Our proposed method is based on the anatomical information and mathematical morphology tools used in the image processing field. At first, we try to remove the spine by applying morphological filters. This first step makes the extraction of interest regions easier. This step is fulfilled by using various transformations such as the geodesic reconstruction. In the second step, we apply the watershed algorithm controlled by marker for kidney segmentation. The validation of the developed algorithm is done using several images. Obtained results show the good performances of our proposed algorithm.

scholarly journals Computational Efficient Method for Denoising of Salt and Pepper Noise for Image Enhancement

2020 ◽  
Vol 8 (6) ◽  
pp. 538-543
Keyword(s):  
Image Processing ◽  
Image Quality ◽  
Median Filter ◽  
Noise Removal ◽  
Input Image ◽  
Filter Method ◽  
Pixel Intensity ◽  
Feature Information ◽  
Salt And Pepper

Digital Image processing is basically a computer-algorithm which is used to enhance the quality of image to understand the feature of image and exact the meaningful features information from image. Image processing has wider range of algorithms to be applied to the input image and can escape the difficulty as the signal distortion and add noise in input image at the time of processing of images. Noises affect the image visualization and degraded the image quality, sometimes chaotic variation in value of pixel intensity, lighting effect or because of poor contrast, image can’t be used directly because many time interest feature information not received as output that’s one reason image processing is significant for removal of noise from images, so noise removal is becomes trending field in image processing. Median filter method is one of most popular method to eradicate the effect of noise from image and it enhances the image quality to take meaningful feature easily from image. In this paper removing of noise using median filter to enhance the image quality is discussed, also the importance and applications of enhancement technique are covered. Parameter PSNR and MSE is also used to analysis the image quality along with the visualization of image. Simulation results show that Median filter gives good outcome for salt & pepper noise as compare to other filtering method. MATLAB software is used as simulation tool.

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