Papers by Keyword: Median Filter

Abstract: The rapid growth in Covid-19 cases increases the burden on health care services all over the world. Hence, a quicker and accurate diagnosis of this disease is essential in this situation. To get quick and accurate results, X-ray images are commonly used. Deep Learning (DL) techniques have reached a high position since they provide accurate results for medical imaging applications and regression problems. However the pre-processing methods are not successful in eliminating the impulse noises and the feature extraction technique involving filtering methods did not yield good filter response. In this paper, Covid-19 X-ray images were classified using the Fuzzy Gabor filter and Deep Convolutional Neural Network (DCNN). Initially the Chest X-ray images are pre-processed using Median Filters. After pre-processing, a Fuzzy Gabor filter is applied for feature extraction. Local vector features were first extracted from the given image using the Gabor filter, taking these vectors as observations. The orientation and wavelengths of the Gabor filter were fuzzified to improve the filter response. The extracted features are then trained and classified using the DCNN algorithm. It classifies the chest X-ray images into three categories that includes Covid-19, Pneumonia and normal. Experimental results have shown that the proposed Fuzzy Gabor-CNN algorithm attains highest accuracy, Precision, Recall and F1-score when compared to existing feature extraction and classification techniques.

Abstract: Aiming at the problem that traditional median filter algorithm cannot process collected images quickly and efficiently, this paper adopts improved median filter and makes use of advantages, such as fast running speed, parallel running of inner program, to design an image preprocessing system with high real time ability and high flexibility. At last, compared with MATLAB median filter simulation figure and multilevel median filter, it has shown that using FPGA to process and improve median filter can not only conduct median filter to images successfully, but also has the ability of fast operation speed and low energy consumption.

Abstract: Based on the principle of the image enhancement, various image enhancement methods are introduced, analyzed and studied. Because image enhancement is closely related to the property of the interested target, the habit of observers and the specific processing goal, the image enhancement is only aimed at the given process goal, too. According to different images, these image enhancement methods are simulated by the MATLAB tools. Through comparing the test results, the results show that different methods will give different effects. Without a common image enhancement method is suitable for various occasions.

Abstract: The Hilbert-Huang Transform (HHT) is a new time-frequency analysis with adaptability and orthogonality, but it is rather weak in terms of noise resistance, even low noise can disturb the HHT result greatly. The paper launches an investigation on how noises affect the HHT result and proposes the method to solve the problem. The analytic framework for HHT is first introduced, the feature of the test signal is extracted by HHT. Median filter is adopted to reduce the frequency leakage of certain signal component caused by white noise. The method proposed is experimentally simulated and the results demonstrate its effectiveness.

Abstract: In order to improve the edge detection efficiency, and decrease the impulse noise impact on the edge detection, a new edge detection algorithm for impulse noise image is proposed. The algorithm combined the median filter idea, and used cross convolution template to calculate image gradients of horizontal and vertical direction. First, detected the point that will attend gradient calculation by threshold, for the noise point, its value will be replaced by median of those points associated with it in detection window. For non-noise point, we directly calculate gradient it initial pixel values. Experiments show that, the new algorithm has strong suppressing noise ability for images corrupted by impulse noise, the extracted image edge is also fine, the outline is clear, the algorithm is better than the traditional Sobel algorithm and the wavelet transform algorithm in noise suppression performance and the detection efficiency, it has strong practicability.

Abstract: This paper takes the calibration method of acceleration sensor’s data filtering as the research subject. It adopts the modified selective filtering method based on the sigma judgment and the zero-point calibration method with the fluctuating wind as the starting and ending point, to correct the acceleration data ideally and to make the images more realistic. Selective filtering method also can be utilized in the image denosing.

Abstract: An image denoising method based on spatial filtering is proposed on order to overcoming the shortcomings of traditional denoising methods in this paper. The method combined mean mask algorithm with median filtering technique is able to replace the gray values of noisy image pixel by the mean or median value in its neighborhood mask matrix and highlight the characteristic value of the image. Peak signal to noise ratio and mean square error are used as the evaluation index in this method and comparison between mean filter and median filter is done. The experimental results show that this denoising system makes the images have a high signal to noise ratio and integrity of edge details and take into account real-time, and fast response characteristic of the system.

Abstract: An image compression denoising method based on median filter and wavelet transform is proposed in order to overcoming shortcomings of traditional methods of image processing in this paper. This method combined hardware parallelism with software technology is enable to achieve image compression denoising and take into account algorithm validation, and fast response of the system. An real-time image processing system is design by this method. Design and hardware implementation of fast median filtering algorithm based on EP1C12 FPGA chip is realized and software simulation of median filter and wavelet transform is done. The experimental results show that this system has advantages of fast response characteristic, less time consuming and high signal to noise ratio.

Abstract: Reliable detection of abnormal vessels in color fundus image is still a great issue in medical image processing. An Efficient and robust approach for automatic detection of abnormal blood vessels in digital color fundus images is presented in this paper. First, the fundus images are preprocessed by applying a 3x3 median filter. Then, the images are segmented using a novel morphological operation. To classify these segmented image into normal and abnormal, seven features based on shape, contrast, position and density are extracted. Finally, these features are classified using a non-linear Support Vector Machine (SVM) Classifier. The average computation time for blood vessel detection was less than 2.4sec with a success rate of 99%. The performance of our proposed method is measured on publically available DRIVE and STARE database.

Abstract: Digital image processing is a kind of technology which employs a certain algorithm to realize image processing through computer algorithm. The powerful capacity of computing and graphics displaying function of Matlab makes image processing becomes more simple and intuitive. This paper introduces the basic knowledge of dealing with noise in digital image, expounds the neighborhood average method, median filtering in detail, and low pass filtering and other typical of eliminating noise method , and at the same time analyzes and compares the characteristics of several typical methods by use of the software Matlab.