scholarly journals A Biased Median Filtering Algorithm for Segmentation of Intestinal Cell Gland Images

2006 ◽  
Vol 6 ◽  
pp. 200-220 ◽  
Author(s):  
Hai-Shan Wu ◽  
Joan Gil
Keyword(s):  
Median Filter ◽  
Goblet Cells ◽  
Segmentation Algorithm ◽  
Intestinal Cell ◽  
Median Filtering ◽  
Median Filters ◽  
Filtering Algorithm ◽  
Identical Dimension ◽  
Intestine Cell ◽  
Image Segmentation Algorithm

In this paper, we introduce a biased median filtering image segmentation algorithm for intestinal cell glands consisting of goblet cells. While segmentation of individual cells are generally based on the dissimilarities in intensities, textures, and shapes between cell regions and background, the proposed segmentation algorithm of intestine cell glands is based on the differences in cell distributions. The intestine cell glands consist of goblet cells that are distributed in the chain-organized patterns in contrast to the more randomly distributed nongoblet cells scattered in the bright background. Four biased median filters with long rectangular windows of identical dimension, but different orientations, are designed based on the shapes and distributions of cells. Each biased median filter identifies a part of gland segments in a particular direction. The complete gland regions are the combined responses of the four biased median filters. A postprocessing procedure is designed to reduce the defects that may occur when glands are located very close together and to narrow the gapping areas because of the thin distribution of goblet cells. Segmentation results of real intestinal cell gland images are provided to show the effectiveness of the proposed algorithm.

scholarly journals Otsu’s Image Segmentation Algorithm with Memory-Based Fruit Fly Optimization Algorithm

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Ruishuai Chai
Keyword(s):  
Genetic Algorithm ◽  
Median Filter ◽  
Image Interpolation ◽  
Good Effect ◽  
Fruit Fly Optimization Algorithm ◽  
Median Filtering ◽  
Grayscale Image ◽  
Interpolation Algorithm ◽  
Filtering Algorithm ◽  
Grayscale Images

In this paper, the most common pepper noise in grayscale image noise is investigated in depth in the median filtering algorithm, and the improved median filtering algorithm, adaptive switching median filtering algorithm, and adaptive polar median filtering algorithm are applied to the OTSU algorithm. Two improved OTSU algorithms such as the adaptive switched median filter-based OTSU algorithm and the polar adaptive median filter-based OTSU algorithm are obtained. The experimental results show that the algorithm can better cope with grayscale images contaminated by pretzel noise, and the segmented grayscale images are not only clear but also can better retain the detailed features of grayscale images. A genetic algorithm is a kind of search algorithm with high adaptive, fast operation speed, and good global space finding ability, and it will have a good effect when applied to the threshold finding of the OTSU algorithm. However, the traditional genetic algorithm will fall into the local optimal solution in different degrees when finding the optimal threshold. The advantages of the two interpolation methods proposed in this paper are that one is the edge grayscale image interpolation algorithm using OTSU threshold adaptive segmentation and the other is the edge grayscale image interpolation algorithm using local adaptive threshold segmentation, which can accurately divide the grayscale image region according to the characteristics of different grayscale images and effectively improve the loss of grayscale image edge detail information and jagged blur caused by the classical interpolation algorithm. The visual effect of grayscale images is enhanced by selecting grayscale images from the standard grayscale image test set and interpolating them with bilinear interpolation, bucolic interpolation, NEDI interpolation, and FEOI interpolation for interpolation simulation validation. The subjective evaluation and objective evaluation, as well as the running time, are compared, respectively, showing that the method of this paper can effectively improve the quality of grayscale image interpolation.

Study on an Improved Median Filtering Algorithm

2014 ◽  
Vol 989-994 ◽  
pp. 2273-2277
Author(s):  
Heng Zhang ◽  
Min Gao ◽  
Hai Long Ren
Keyword(s):  
Image Processing ◽  
Processing Speed ◽  
Median Filter ◽  
Median Filtering ◽  
Matlab Simulation ◽  
Filtering Algorithm ◽  
Noise Point ◽  
Real Time Image ◽  
Linear Smoothing ◽  
Time Image

Median filter is a very effective method of non-linear smoothing filtering. However, the data ordering for traditional median filtering (TMF) is very time-consuming and hardly satisfy real-time image processing. This article proposes a kind of fast median filtering algorithm based on grey histogram, the filter seeks the median through the grey histogram of mask window, not the numeric sort, which decreases the comparison times. Moreover, the update of histogram by using the overlap of mask window increases the arithmetic processing speed. Meanwhile, in the proposed algorithm, the two-level self-adapting threshold comparison, with a higher precision of detection, is used to implement the inspection of noise point and improve the image quality and increase the signal-noise ratio by processing the noise point and non-noise point respectively. The experiments by matlab simulation can prove the availability of this algorithm.

Application of Improved Median Filtering Algorithm to Image De-Noising

2014 ◽  
Vol 998-999 ◽  
pp. 838-841
Author(s):  
Wen Long Jiang ◽  
Guang Lin Li ◽  
Wei Bing Luo
Keyword(s):  
Impulse Noise ◽  
Median Filter ◽  
Median Filtering ◽  
Real Time Processing ◽  
Time Processing ◽  
Filtering Algorithm ◽  
Weighted Median ◽  
The Mean ◽  
Weighted Median Filter ◽  
Mean Filtering

Based on the shortcomings of standard median filtering and combined with the mean filtering, this paper puts forward two improved median filtering algorithms referred as the weighted fast median filtering algorithm and the weighted adaptive median filtering algorithm. The experiment results with MATLAB show that weighted fast median filtering algorithm has a significant effect on low-density impulse noise, and accelerates the speed of real-time processing. The weighted median filter could effectively eliminate the high-density impulse noise from polluted images, and better maintains the details of the original image.

scholarly journals CONSTANT TIME SCANNING AND BETTER EDGE PRESERVATION FOR BETTER PERFORMING AND QUALITY OF MEDIAN FILTER

2012 ◽  
pp. 114-116
Author(s):  
RITWIK SHARMA ◽  
SHUBHAM HARNAL
Keyword(s):  
Image Processing ◽  
Median Filter ◽  
Median Filtering ◽  
Edge Preservation ◽  
Median Filters ◽  
Novel Approach ◽  
Time Scanning ◽  
The Cost ◽  
Salt And Pepper

The median filter is an important filter in many image processing algorithms and especially in removal of salt and pepper noise. Traditional median filters either focus on improving the performance or the quality of the median filtering. Generally, the methods which optimize performance do so at the cost of quality and vice-versa. In this paper a novel approach to median filtering is presented providing both better performance and quality without sacrificing either. The analysis is presented with respect to image processing and the results obtained are presented in tabular form.

scholarly journals Reconstruction of Corrupted Image From Salt And Pepper Noise From Median Filter

2012 ◽  
pp. 4-6
Author(s):  
Vishal Gautam ◽  
Tarun Varma
Keyword(s):  
Median Filter ◽  
Median Filtering ◽  
Salt And Pepper Noise ◽  
Filtering Algorithm ◽  
Noise Density ◽  
Mean Filter ◽  
Salt And Pepper ◽  
Image Details ◽  
Lower Noise ◽  
Better Than

- Inthis paper,we propose an improved median filtering algorithm. Here, we introduced salt and pepper noise for the image corruption and reconstruct original image using different filters i.e. mean, median and improved median filter. The performance of improved median filter is good at lower noise density levels.The mean filter suppresses little noise and gets the worst results.The experimental resultsshow that our improved median filter is better than previousmedian filterfor lower noise density (upto 60%). It removes most of the noises effectively while preserving image details very well.

Impulse Noise Filtering Algorithm Based on Dual Threshold Criterion

2013 ◽  
Vol 437 ◽  
pp. 849-852 ◽  
Author(s):  
Yu Lan Wei ◽  
Bing Li ◽  
Jian Dong Li ◽  
Ying Ying Fan
Keyword(s):  
Impulse Noise ◽  
Surface Defect ◽  
Median Filter ◽  
Noise Filtering ◽  
Median Filtering ◽  
Filtering Algorithm ◽  
Obvious Advantage ◽  
Noise Point ◽  
Threshold Criterion ◽  
Good For

In order to filter the impulse noise existing in the weld surface defect images, the traditional median filtering will dim image, even destroy some details in the image, We put forward a new filtering algorithm based on dual threshold Criterion. This way distinguishes the noise point and signal location in the first, and it only doing median filter to the noise point. Lastly, it solves the image’s boundary. We can find it when compared to the traditional median filtering and modified extremum median filtering, the way in this article can be good for filtration, and can keep the image’s details, which have obvious advantage.

scholarly journals Adaptive algorithm for reducing pulse noise level in images from CCTV cameras

2021 ◽  
pp. 21-27
Author(s):  
А. V. Sadchenko ◽  
O. A. Kushnirenko ◽  
A. V. Troyanskiy ◽  
Yu. A. Savchuk
Keyword(s):  
Impulse Noise ◽  
Reconstructed Image ◽  
Correct Identification ◽  
Median Filtering ◽  
Noise Variance ◽  
Discrete Elements ◽  
Noise Levels ◽  
Objective Criterion ◽  
Median Filters ◽  
Filtering Algorithm

An optical signal is usually converted into an electrical one by using photosensitive matrices with a large number of discrete elements based on charge-coupled device (CCD) technology or CMOS technology. One of the disadvantages of CCD and CMOS technologies is the impulse conversion noise that appears on digitized images, impairing visual perception and significantly reducing the likelihood of correct identification in pattern recognition tasks. Traditionally, impulse noise is removed from images using median filters with a fixed aperture within each iteration of full-format processing. However, such filters reduce the sharpness of the reconstructed image at high noise levels or insufficiently suppress the interference under the same noise conditions. These setbacks call for a need to develop an adaptive median filtering algorithm, which would produce a reconstructed image as a joint result of processing with median filters with different apertures. The essence of this algorithm is to select image areas with different noise levels and process these areas with filters with different apertures. As an objective criterion for assessing the efficiency of the proposed filtering algorithm, the authors used the criterion of the maximum correlation coefficient between noise-free and non-noisy images at various values of the noise variance. The mathematical modeling performed in this study allowed finding that with an increase in the impulse noise variance, the gain of the adaptive median filtering algorithm increases exponentially, in comparison with the algorithms using the filters with a fixed aperture value. The proposed algorithm can be used for pre-preprocessing images intended for recognition by machine vision systems, scanning text, and improving subjective image characteristics, such as sharpness and contrast.

scholarly journals USE OF MEDIAN FILTERS IN PROCESSING EXPERIMENTAL DATA ON FLUCTUATION DYNAMICS OF FLOATING STRUCTURES OF VARIOUS TYPES

2018 ◽  
pp. 7-15
Author(s):  
Evgeniy Mikhailovich Gramuzov ◽  
Olga Aleksandrovna Ivanova
Keyword(s):  
Experimental Data ◽  
High Frequency ◽  
Narrow Band ◽  
Median Filter ◽  
Spectral Characteristics ◽  
Frequency Noise ◽  
Median Filtering ◽  
High Frequency Noise ◽  
Median Filters ◽  
Angular Oscillations

The paper is devoted to the analysis of increasing the accuracy of the spectral characteristics obtained after processing experimental data of the vibrations of a physical model of the offshore drilling platform in the experimental tank. The increase in accuracy is provided by the recognition and correction of breakdowns and errors in the course of median filtering of vibration recordings of different positions of the platform relative to the propagation of waves in the tank and with different deepening of the platform support columns. The median filters are known to belong to nonlinear transformations; therefore, their characteristics are determined by the structure of their processing procedures. In this case, these are narrow-band, random oscillations with maximum spectra at wave frequencies, at fundamental frequency of the platform oscillation and harmonics, at frequencies that are multiple to the fundamental frequency. To use median filters in processing such signals, the amplitude-frequency characteristics of the median filter were determined as a function of the ratio of the characteristic period of oscillations to the equivalent filter base, i.e. to multiplying a number of points in the filter core by the discreteness of measurements. As a result, the condition for the effective application of median filtering in the processing of narrow-band random signals has been determined, which is used in analyzing the spectra of the processes under study. There are given characteristic examples of the spectra of angular oscillations (by roll and depth) of a semisubmersible platform with different drafts. It has been shown that at maximum draft, when the model is most stable, median filtering suppresses high-frequency noise components of the vibration spectra by 5-10 times and, at the same time, the main features of the spectra are not distorted, i.e. the main energy-carrying components are reproduced without visible distortion. In the other limiting case, with minimum platform draft, changes in the high-frequency part of the spectra are negligible due to increased intensity of angular oscillations. In this case, the oscillation spectra also reproduce the main features of dynamic processes. These data indicate the selective sensitivity of the median filter to the structure of the operating procedure and possibility of improving the accuracy of reproduction of spectral characteristics by suppressing the high-frequency noise components of a signal.

An Improved Hybrid Median-Mean Filter Algorithm

2014 ◽  
Vol 701-702 ◽  
pp. 288-292
Author(s):  
Fang Jia ◽  
De Cheng Xu ◽  
Xin Fu
Keyword(s):  
Gaussian Noise ◽  
Median Filter ◽  
Experimental Results ◽  
Median Filtering ◽  
Filtering Algorithm ◽  
Mean Filter ◽  
Image Details ◽  
Mean Filtering

In the process of imaging, digitalization and transmission, images are generally contaminated by Gaussian noise and salt & pepper noise, which cannot be eliminated completely at the same time only by Mean filter or Median filter. Aiming at solving this problem, an improved hybrid median-mean filter algorithm based on the Improved Median Filtering (IMF) algorithm is proposed in this paper. The experimental results show that the new algorithm shows better performance than either Median filtering algorithm or Mean filtering algorithm, which can not only get rid of Gaussian noise and salt & pepper noise simultaneously, but also minimize the contradictions between noise erasing and image details protecting effectively.

scholarly journals RESEARCH OF MEDIAN FILTERING OF ONE-DIMENSIONAL SIGNALS

2020 ◽  
pp. 88-102
Author(s):  
Andriy Vydmysh ◽  
Oleksandr Voznyak ◽  
Igor Kupchuk ◽  
Dmitry Boyko
Keyword(s):  
Signal Processing ◽  
Digital Signal Processing ◽  
Digital Filters ◽  
Median Filter ◽  
Digital Signal ◽  
Median Filtering ◽  
One Dimensional ◽  
Electrical Signals ◽  
Median Filters

This paper considers the principles of digital signal processing, the general provisions of digital filtering, existing methods of noise filtering in electrical signals, the median filters of one-dimensional signals are studied in detail. To solve this problem, the classification of digital signal processing tools is presented. Since the most effective for filtering noise in electrical signals are digital filters, they are given the most attention. The main purpose of signal filtering is the need to extract the information contained in them. This information, which is usually present in the amplitude of the signal (absolute or relative), in frequency or spectral composition, in phase or in the relative time dependences of several signals. The classification of existing digital filters is carried out. For further development, a median filter was selected, which belongs to the class of heuristics and is one of the most effective in filtering signals from impulse noise and white noise. Highlighting the advantages and disadvantages, a review of existing software that implements the median filter. It is established that the urgent task is to increase the processing speed and reduce resource costs in the implementation of such filters, developed an algorithm for fast median filtering, conducted an experimental test of software-implemented median filters with different apertures at different levels of fluctuation noise. This program meets all the requirements of modern norms and standards, allows its practical use to solve real problems of signal processing. In order to increase the speed of information processing, a median filtering algorithm based on difference matrices using the threshold saturation function has been developed. Developed software that implements the proposed algorithm. Schemes of the main program, reading of values of a signal from a file, filtering, sorting of data on amplitude, a choice of a window of elements, a choice of the registered values are presented. The conditions of data registration and ADC parameters to ensure efficient operation of filters are also defined

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