Optimum Image Filters for Various Types of Noise

In this paper, the quality performance of several filters in restoration of images corrupted with various types of noise has been examined extensively. In particular, Wiener filter, Gaussian filter, median filter and averaging (mean) filter have been used to reduce Gaussian noise, speckle noise, salt and pepper noise and Poisson noise. Many images have been tested, two of which are shown in this paper. Several percentages of noise corrupting the images have been examined in the simulations. The size of the sliding window is the same in the four filters used, namely 5x5 for all the indicated noise percentages. For image quality measurement, two performance measuring indices are used: peak signal-to-noise ratio (PSNR) and structural similarity (SSIM). The simulation results show that the performance of some specific filters in reducing some types of noise are much better than others. It has been illustrated that median filter is more appropriate for eliminating salt and pepper noise. Averaging filter still works well for such type of noise, but of less performance quality than the median filter. Gaussian and Wiener filters outperform other filters in restoring mages corrupted with Poisson and speckle noise

Effect of kernel size on Wiener and Gaussian image filtering

In this paper, the effect of the kernel size of Wiener and Gaussian filters on their image restoration qualities has been studied and analyzed. Four sizes of such kernels, namely 3x3, 5x5, 7x7 and 9x9 were simulated. Two different types of noise with zero mean and several variances have been used: Gaussian noise and speckle noise. Several image quality measuring indices have been applied in the computer simulations. In particular, mean absolute error (MAE), mean square error (MSE) and structural similarity (SSIM) index were used. Many images were tested in the simulations; however the results of three of them are shown in this paper. The results show that the Gaussian filter has a superior performance over the Wiener filter for all values of Gaussian and speckle noise variances mainly as it uses the smallest kernel size. To obtain a similar performance in Wiener filtering, a larger kernel size is required which produces much more blur in the output mage. The Wiener filter shows poor performance using the smallest kernel size (3x3) while the Gaussian filter shows the best results in such case. With the Gaussian filter being used, similar results of those obtained with low noise could be obtained in the case of high noise variance but with a higher kernel size

Automated reduction the speckle noise of the panoramic ultrasound images of Muscles and Tendons

Peer reviewedPublisher PD

The Encyclopedia of Neutrosophic Researchers - vol. 1

This is the first volume of the Encyclopedia of Neutrosophic Researchers, edited from materials offered by the authors who responded to the editor’s invitation. The authors are listed alphabetically. The introduction contains a short history of neutrosophics, together with links to the main papers and books. Neutrosophic set, neutrosophic logic, neutrosophic probability, neutrosophic statistics, neutrosophic measure, neutrosophic precalculus, neutrosophic calculus and so on are gaining significant attention in solving many real life problems that involve uncertainty, impreciseness, vagueness, incompleteness, inconsistent, and indeterminacy. In the past years the fields of neutrosophics have been extended and applied in various fields, such as: artificial intelligence, data mining, soft computing, decision making in incomplete / indeterminate / inconsistent information systems, image processing, computational modelling, robotics, medical diagnosis, biomedical engineering, investment problems, economic forecasting, social science, humanistic and practical achievements

Computer-Aided Diagnosis of Thyroid Nodule: A Review

ABSTRAC

Improved graph cut model with features of superpixels and neighborhood patches for myocardium segmentation from ultrasound image

Ultrasound (US) imaging has the technical advantages for the functional evaluation of myocardium compared with other imaging modalities. However, it is a challenge of extracting the myocardial tissues from the background due to low quality of US imaging. To better extract the myocardial tissues, this study proposes a semi-supervised segmentation method of fast Superpixels and Neighborhood Patches based Continuous Min-Cut (fSP-CMC). The US image is represented by a graph, which is constructed depending on the features of superpixels and neighborhood patches

An image processing decisional system for the Achilles tendon using ultrasound images

The Achilles Tendon (AT) is described as the largest and strongest tendon in the human body. As for any other organs in the human body, the AT is associated with some medical problems that include Achilles rupture and Achilles tendonitis. AT rupture affects about 1 in 5,000 people worldwide. Additionally, AT is seen in about 10 percent of the patients involved in sports activities. Today, ultrasound imaging plays a crucial role in medical imaging technologies. It is portable, non-invasive, free of radiation risks, relatively inexpensive and capable of taking real-time images. There is a lack of research that looks into the early detection and diagnosis of AT abnormalities from ultrasound images. This motivated the researcher to build a complete system which enables one to crop, denoise, enhance, extract the important features and classify AT ultrasound images. The proposed application focuses on developing an automated system platform. Generally, systems for analysing ultrasound images involve four stages, pre-processing, segmentation, feature extraction and classification. To produce the best results for classifying the AT, SRAD, CLAHE, GLCM, GLRLM, KPCA algorithms have been used. This was followed by the use of different standard and ensemble classifiers trained and tested using the dataset samples and reduced features to categorize the AT images into normal or abnormal. Various classifiers have been adopted in this research to improve the classification accuracy. To build an image decisional system, a 57 AT ultrasound images has been collected. These images were used in three different approaches where the Region of Interest (ROI) position and size are located differently. To avoid the imbalanced misleading metrics, different evaluation metrics have been adapted to compare different classifiers and evaluate the whole classification accuracy. The classification outcomes are evaluated using different metrics in order to estimate the decisional system performance. A high accuracy of 83% was achieved during the classification process. Most of the ensemble classifies worked better than the standard classifiers in all the three ROI approaches. The research aim was achieved and accomplished by building an image processing decisional system for the AT ultrasound images. This system can distinguish between normal and abnormal AT ultrasound images. In this decisional system, AT images were improved and enhanced to achieve a high accuracy of classification without any user intervention