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

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.

THE TASK OF POSITIONING THE WORKING MECHANISMS CRM WITH A GPS INTENSIFIER

The study of the task of positioning the working mechanisms of construction and road machines (CRM) of using GPS intensifier was carried out. The analysis of existing researches and publications, in which the main problem is highlighted, namely that the task of positioning the working mechanisms CRM at this time is not enough. As a result of the analysis the purpose of research is set, namely: to increase of functioning efficiency mechanisms CRM with working environment using mathematical models and adaptation algorithm in a limited time decision. The task of monitoring parameters using Kalman or Wiener filters which to take machine vibrations into account, deviations in working operations, changes in weight, etc. have been substantiated. The use of a GPS intensifier makes it possible to predict the work of actuators CRM in real time. The result of the research is algorithm of positioning the working mechanisms CRM: determination of the location of the base CRM in a 3-dimensional coordinate system; filtering measurements; predicting the position of the working mechanism. The originality lies in the fact that the using Kalman or Wiener filters allows to describe the trajectory in the coordinate system of the base machine in accordance with the point measurement, and describe the relationship between changed coordinates, which makes it possible to model and predict the workflow.

Analysis of the Wiener filters application to the spectral fluctuation patterns segmentation

The Wiener filter is an adaptive filter which able to produce the desired estimates. Besides, this filter can also suppress noise in digital signal processing. This study aims to segment the fluctuation pattern, which results from data acquisition from a capacitive sensor with the object H2O. The fluctuation pattern to be processed is the High Fluctuation (HF) pattern by dividing the pattern into several segments according to the input frequency. It aims to see in more detail and clearly the state of each segmentation of the pattern. The results will show noise attenuation and suppression after filtering with a Wiener filter. The Signal to Noise Ratio (SNR) value will also be analyzed, which shows that the signal quality is getting better after applying the Wiener filter. Then, the analysis of the Mean Square Error (MSE) results can provide more consistent results with a smaller average error.

Nonlinear average stochastic resonance for image enhancement

How to extract useful information from noised image is always an important issue for image processing. Many methods have been proposed in image enhancement field. However, in these methods the noise is usually considered as harmful and should be removed as much as possible. Stochastic resonance is a very different method, in which the noise is regarded as a driver to push the stochastic resonance system to output enhanced image. In this paper, the cumulative gain is introduced and the sequence average is used to enhance the original image information which hidden in a noised image sequence produced by bistable stochastic resonance. We present the one-dimensional and two-dimensional stochastic resonance methods and discuss their performance in this paper. Experiments illustrate that the one-dimensional average stochastic resonance has the best performance considering the indicator PSNR and SSIM. Compared with traditional filters such as median and Wiener filters, the proposed methods have significant advantages.