Hybrid Median Filter Research Articles

An Efficient Approach for Lung Nodule Detection on CT Images

The normal reason for death among individuals all through mankind is lung tumor. Early identification of lung malignancy can expand the shot of survival among individuals. In this way, there is a wonderful prerequisite for another development to investigate the lung development in its starting circumstances. Image processing strategies give a better than average quality mechanical assembly to upgrading the manual examination. In this paper, Hybrid median filter is utilized for picture pre-processing. For segmentation, Region Growing based Segmentation strategy is utilized. In feature extraction, we utilize Statistical and Shape based Feature Extraction are utilized. Back Propagation Network (BPN) with Particle Swarm Optimization (PSO) is connected for order of Lung Nodule. CT (computed tomography) scan image is appropriate for lung growth diagnosis. This paper is to actualize feature extraction and classification of lung growth knob utilizing image processing systems. To execute the count, MATLAB writing computer programs is made. This framework can help radiologists and pros to know the condition of ailments at starting periods with more advanced detection and targeting ways that several researchers will certainly follow and hopefully succeed and to keep up a key separation from honest to goodness contamination stages for lung ailment patients.

Intelligent traffic video surveillance and accident detection system with dynamic traffic signal control

Enormous advance has proven throughout the years in the area of traffic surveillance by the growth of intelligent traffic video surveillance system. In the current work, through the traffic videos, the traffic video surveillance automatically keyed out the vehicles like ambulance and trucks, which in turn assisted us in directing the vehicles at the time of emergency. Nevertheless, it doesn’t provide us a vital solution for the regulating the traffic. Moreover, this idea just identifies the vehicles, but it couldn’t notice the accidents expeditiously. Therefore in the proposed work, expeditious traffic video surveillance and monitoring system are presented along with dynamic traffic signal control and accident detection mechanism. Hybrid median filter has been utilized at the beginning for pre-processing of traffic videos, and to remove the noise. Hybrid support vector machine (SVM with extended Kalman filter) has been utilized to chase the vehicles. Next, the histogram of flow gradient features are drew-out to categories the vehicles. According to the traffic density and through video files, vehicles are computed, and then for emergency vehicles, the traffic signal gets switched dynamically. To realize the arrival of ambulances, the cameras have been set to catch traffic videos minimum at 500 m of the signal and deep learning neural networks has been employed. Hence dynamic signal control has been incorporated expeditiously. Likewise, multinomial logistic regression has been utilized in real-time live streaming videos, to identify the accidents correctly. The observational solution shows that the proposed intelligent traffic video surveillance system render expeditious dynamic control of traffic signals and it raises the identification of accidents correctly.

An enhanced PSO-DEFS based feature selection with biometric authentication for identification of diabetic retinopathy

Recently, automatic diagnosis of diabetic retinopathy (DR) from the retinal image is the most significant research topic in the medical applications. Diabetic macular edema (DME) is the major reason for the loss of vision in patients suffering from DR. Early identification of the DR enables to prevent the vision loss and encourage diabetic control activities. Many techniques are developed to diagnose the DR. The major drawbacks of the existing techniques are low accuracy and high time complexity. To overcome these issues, this paper proposes an enhanced particle swarm optimization-differential evolution feature selection (PSO-DEFS) based feature selection approach with biometric authentication for the identification of DR. Initially, a hybrid median filter (HMF) is used for pre-processing the input images. Then, the pre-processed images are embedded with each other by using least significant bit (LSB) for authentication purpose. Simultaneously, the image features are extracted using convoluted local tetra pattern (CLTrP) and Tamura features. Feature selection is performed using PSO-DEFS and PSO-gravitational search algorithm (PSO-GSA) to reduce time complexity. Based on some performance metrics, the PSO-DEFS is chosen as a better choice for feature selection. The feature selection is performed based on the fitness value. A multi-relevance vector machine (M-RVM) is introduced to classify the 13 normal and 62 abnormal images among 75 images from 60 patients. Finally, the DR patients are further classified by M-RVM. The experimental results exhibit that the proposed approach achieves better accuracy, sensitivity, and specificity than the existing techniques.

Combination of hybrid median filter and total variation minimisation for medical X‐ray image restoration

X-ray image restoration is a difficult problem aimed at improving image quality for visual inspection. Preservation of edges and relevant image information are important for the diagnosis of diseases by physicians or radiologists. In this research, the authors combined a hybrid median filter (HMF) and total variation (TV) minimisation to restore medical X-ray images. A standard TV minimisation preserves the edges or boundaries of the reconstructed image but also includes false edges. A modified HMF was employed on a Lagrange multiplier associated with a gradient constraint to remove these false edges during the multiplier update. The TV minimisation unconstrained problem was transformed into a constrained form. An augmented Lagrangian method with a variable penalty term was used to handle the constraints in the form for faster convergence, and the alternative direction method solved the subproblems iteratively. The proposed techniques provide improved convergence speed and restore good quality medical X-ray images to remove false edges and the staircase effect.

An efficient multi-RVM classification-based ultrasound lung image retrieval approach

This paper proposes an efficient Multi-Relevance Vector Machine (multi-RVM) classification-based ultrasound image retrieval approach for retrieval of lung images relevant to the query image. In our proposed work, a hybrid median filter is used for filtering the training and testing ultrasound lung image. Extraction of feature in the lung image is performed by using the Tamura features and convoluted grey-level co-occurrence matrix approach. The particle swarm optimisation combined differential evolution feature selection approach performs selection of minimum set of features relevant to the query image. Multi-RVM-based classification technique is used to identify the types of lung diseases. Finally, the Hamming-distance-based retrieval technique performs retrieval of similar and relevant lung images from the database. From the performance analysis result, it is clearly evident that the proposed approach achieves better performance in terms of accuracy, sensitivity and specificity, when compared to the existing classification techniques.

Noise Reduction in Compressed Images Using Improved Fuzzy Transform Technique

In the area of digital image compression, computer algorithms are used to perform processing of images and compression. It deals with developing a digital system that perform operations on digital image. It has many advantages using in digital camera, film, Satellite, X-ray and many more applications. Image compression is a technique used to save the storage space normally used to compress images and videos. Number of compression algorithms are used like run length encoding, huffman coding, discrete cosine transform, vector quantization, fuzzy transform. This gives a brief idea on improved fuzzy technique to reduce noise in compressing image. There are so many techniques for compression but in this only present the techniques of improved fuzzy method to reduce noise and compressed the image by using edge detection. The main idea behind applying this is we have to preserve the well significant edges as Jpeg is the popular standard but at low bit rate Jpeg exhibits blocking artifacts means noisy effects that affect the visual image quality so to produce high visual quality image at low bit rate ,the algorithm is efficient and simple. The proposed algorithm consists of three steps. First, image is preprocessed using competitive fuzzy edge detection. Second, based on edge information image is compressed and decompressed using improved fuzzy transform. Third, reconstructed image is post processed using hybrid median filter for artifact reduction. Analysis proves the superiority of proposed algorithm. The results of different number of coefficients are compared with the value of PSNR, MSE of algorithm. After comparison of techniques it is found to be efficient for visualization.

Pac-Man Game using Hand Movement without Keyboard and Mouse

The game “Pac-Man” is a widely famous and well-loved game in video arcades all over the world. In this paper, we propose a function to substitute central points of hand coordinates for direction keys of the game. With this function, we can play the game with several simple hand movements and without a keyboard or a mouse. The movements of a monster in the game could be performed by those of a hand since it can get video images in MFC(Microsoft Foundation Class) dial log by extracting pointers of the hand. To extract skin color, a binary RGB (Red, Green, and Blue) is transformed into a binary YCbCr video image without brightness. Several operations of product, hybrid median filtering, and AND are performed, in order to get a better video image quality. The central point of a hand can be found by use of the mean of two central points after the central point of a minimum boundary rectangle and a hand is found.

An Automotive Approach for Brain Tumor Segmentation Based on Gaussian Distribution and Level Set Method

In recent years a great work of the research in the field of medical imaging was focused on the brain tumor segmentation. In this paper, a novel region based active contour model for Magnetic Resonance Images (MRIs) brain tumor segmentation based on a level set origination is proposed and implemented. The image intensities are explained based on the Gaussian distributions through diverse means and variances. The attained local mean and variances are defined as variables and the Moore Gaussian distributions are described by the level set function. The energy minimization is attained by the curve evolution of level set and the approximation of the local intensity means and variances, it is an iterative process. To handle the intensity inhomogeneities and noise, the means and variances are measured as spatially varying functions. The tumor segmentation is an important early phase to solve the segmentation problem effectively. Hybrid Median Filter (HMF) is used to preserve the edges. Also, Sussman boundary condition is explored to accurately extract the tumor portions rather than the unwanted segmentations. The number of iterations used in this novel framework is quite lesser than the existing approach. Hence, the time taken for proposed tumor segmentation technique is also lesser than the existing method. The proposed system Gaussian Distribution with Level Set Method (GD-LSM) results accurate segmentation, sensitivity and specificity outcomes. Keywords: Active Contour, Brain Tumor Segmentation, Curve Evolution, Gaussian Distributions, Hybrid Median Filter (HMF), Level set, and Magnetic Resonance Images (MRIs).

Characterizing the continuously acquired cardiovascular time series during hemodialysis, using median hybrid filter preprocessing noise reduction.

The clinical characterization of cardiovascular dynamics during hemodialysis (HD) has important pathophysiological implications in terms of diagnostic, cardiovascular risk assessment, and treatment efficacy perspectives. Currently the diagnosis of significant intradialytic systolic blood pressure (SBP) changes among HD patients is imprecise and opportunistic, reliant upon the presence of hypotensive symptoms in conjunction with coincident but isolated noninvasive brachial cuff blood pressure (NIBP) readings. Considering hemodynamic variables as a time series makes a continuous recording approach more desirable than intermittent measures; however, in the clinical environment, the data signal is susceptible to corruption due to both impulsive and Gaussian-type noise. Signal preprocessing is an attractive solution to this problem. Prospectively collected continuous noninvasive SBP data over the short-break intradialytic period in ten patients was preprocessed using a novel median hybrid filter (MHF) algorithm and compared with 50 time-coincident pairs of intradialytic NIBP measures from routine HD practice. The median hybrid preprocessing technique for continuously acquired cardiovascular data yielded a dynamic regression without significant noise and artifact, suitable for high-level profiling of time-dependent SBP behavior. Signal accuracy is highly comparable with standard NIBP measurement, with the added clinical benefit of dynamic real-time hemodynamic information.

Refinement of Enhanced Speech Using Hybrid-Median Filter and Harmonic Regeneration Approach

This study proposes a post-processor to improve the harmonic structure of a vowel in an enhanced speech, enabling the speech quality to be improved. Initially, a speech enhancement algorithm is employed to reduce the background noise for a noisy speech. Hence the enhanced speech is post-processed by a hybrid-median filter to reduce the musical effect of residual noise. Since the harmonic spectra are impacted by background noise and a speech enhancement process, the quality of a vowel is deteriorated. A harmonic regenerated method is developed to improve the quality of post-processed speech. Experimental results show that the proposed method can improve the quality of post-processed speech by adequately regenerating harmonic spectra.

Efficient Lane Detection Algorithm using Different Filtering Techniques

Today, one of the largest areas of research and development in the automobile industry is road safety. Many deaths and injuries occur every year on public roads from accidents. However, the most dramatic fact is that, nearly all of the accidents are caused by driver mistakes. The main goal of the lane detection system is to reduce the number of these accidents. Remarkable amount of the current researches in this field focus on building autonomous driving systems. This research work presents an approach for improving the performance of lane detection algorithm by using different filtering techniques. This paper deals with an efficient ways of noise reduction in the images by using different filtering techniques. The main objective is to design, develop, implement and subsequently simulate an efficient lane detection algorithm which will provide high quality results in the case when noise is present in the signal. The scope of the dissertation is to implement lane detection algorithm without using any filter, to implement lane detection algorithm using median, wiener, and hybrid median filters. And to compare the performance on the basis of accuracy, specificity, BER, PSNR, without and with filters (Median, Wiener, Hybrid median filters). By giving some selected road images, experiments will be taken, that will be useful for performance comparison. A variety of tests will be performed using improved algorithm to test various aspects of the road images. Comparisons will be drawn among proposes strategy with well-known existing algorithms.

基于改进的分块压缩感知红外图像重建

Regarding to the reconstruction problem of an infrared image in compressed sensing, we proposed a modified block compressed sensing method. First, the original infrared image is divided into small blocks, each of which is sampled with a Gaussian random matrix to generate a small amount of measurement data. Second, Spectral Graph Wavelet transform with excellent sparse features is applied into reconstruction process of projected Landweber algorithm, and the hybrid median filter is used for enhancing image smoothness and reducing block artifacts. Finally, the high-quality infrared image satisfied termination conditions is obtained. Experimental results on various types of infrared images show that the proposed method attains much better performance in CS recovery than the conventional ones and can obtain higher quality infrared images.

Improved Image Filtering Using Integrated Hybrid Median Filter & Alpha Trimmed Mean Filter

Image filtering is a crucial part of vision processing as it can remove noise from noisy images. There are many filtering techniques to filter an image. Each filtering technique has its own benefits to filter an image. The overall objective of this paper is to explore the benefits and limits of existing techniques and propose an improved filter which reduces noise from image. It is found that hybrid median filter and alpha trimmed has some potential benefits over existing filters when to reduce salt and pepper noise, but the improved filter can be used as framework for opening further research directions in solving for noise removal.

Integrated Marker-Based Watershed Segmentation with Hybrid median Filter

This paper focuses on marker based watershed segmentation algorithms. As marker based watershed segmentation algorithm causes over segmentation and cause noise in the image produced. So to reduce these problem different researchers has proposed different solutions, but the best solution is to use bilateral filter. The main objective of this paper is to find the gaps in existing literature. The different segmentation techniques are reviewed and found that marker based is best in most of cases because it marks the regions then segment them. But optimizing the marking regions is still an area of research.

Digital Image Filtering Techniques- A survey

This paper presents a review on digital image filtering techniques. The main emphasis is on median filtering and its extended versions like hybrid median filtering, relaxed median filtering etc. It is found that still median filtering demands some sort of enhancements as it is best for salt and pepper noise only. By conducting a survey suitable gaps are found in existing literature. In the end comparison table is also drawn among the existing techniques.

Mingle Face Detection using Adaptive Thresholding and Hybrid Median Filter

detection has many applications in the field of digital image processing like face recognition, face analysis etc. Various techniques have been proposed so far to detect faces in images by different researchers. This paper presents a noble face detection algorithm which integrates the adaptive segmentation and hybrid median filter to improve the performance of face detection algorithms. Single faces noisy images are considered in this research work to evaluate the performance of the proposed algorithm. The results are compared with some well known face detection algorithms. Keywordsdetection, adaptive thresholding, hybrid median filter, image processing, single face image

GPU Accelerated Automated Feature Extraction From Satellite Images

The availability of large volumes of remote sensing data insists on higher degree of automation in feature extraction, making it a need of the hour.The huge quantum of data that needs to be processed entails accelerated processing to be enabled.GPUs, which were originally designed to provide efficient visualization, are being massively employed for computation intensive parallel processing environments. Image processing in general and hence automated feature extraction, is highly computation intensive, where performance improvements have a direct impact on societal needs. In this context, an algorithm has been formulated for automated feature extraction from a panchromatic or multispectral image based on image processing techniques. Two Laplacian of Guassian (LoG) masks were applied on the image individually followed by detection of zero crossing points and extracting the pixels based on their standard deviation with the surrounding pixels. The two extracted images with different LoG masks were combined together which resulted in an image with the extracted features and edges. Finally the user is at liberty to apply the image smoothing step depending on the noise content in the extracted image. The image is passed through a hybrid median filter to remove the salt and pepper noise from the image. This paper discusses the aforesaid algorithm for automated feature extraction, necessity of deployment of GPUs for the same; system-level challenges and quantifies the benefits of integrating GPUs in such environment. The results demonstrate that substantial enhancement in performance margin can be achieved with the best utilization of GPU resources and an efficient parallelization strategy. Performance results in comparison with the conventional computing scenario have provided a speedup of 20x, on realization of this parallelizing strategy.

3D 맵 빌딩을 위한 하이브리드 미디언 필터를 이용한 LRF의 임펄스 잡음 제거

In this paper, a single LRF has been used to produce a 3D map for the mobile robot navigation. The 2D laser scanners are used for mobile robots navigation, where the laser scanner is applied to detect a certain level of area by the straight beam. Therefore it is limited to the usages of 2D obstacle detection and avoidance. In this research, it is designed to complement a mobile robot system to move up and down a single LRF along the yaw axis. During the up and down motion, the 2D data are stacked and manipulated to build a 3D map. Often a single LRF data are mixed with Gaussian and impulse noises. The impulse noises are removed out by the hybrid median filter designed in this research. The 2D data which are improved by deleting the impulse noises are layered to build the 3D map. Removing impulse noises while preserving the boundary is a main advantages of the hybrid median filter which has been used widely to improve the quality of images. The effectiveness of this hybrid median filter for rejecting the impulse noises has been verified through the real experiments. The performance of the hybrid median filter is evaluated in terms of PSNR (Peak Signal to Noise Ratio) and the processing time.

게임 산업에서 손동작 인식을 이용한 팩맨 게임

팩맨(Pacman)은 세계적으로 사랑을 받는 클래식 게임의 종결자로 알려져 있다. 본 논문에서는 클래식 게임인 팩맨을 키보드나 마우스를 사용하지 않고 간단한 손동작만으로 게임을 플레이 할 수 있도록 한다. 즉, 손의 좌표 중심점을 이용하여 방향키를 대신할 수 있도록 한 모션을 이용한 게임이다. 또한 캠을 이용하여 MFC 다이얼로그에 영상을 받아 손에 대한 포인터를 추출하여 손의 움직임에 따라 몬스터의 움직임도 바뀌도록 한다. 본 논문에서 피부색 추출을 위해 RGB 영상을 YCbCbr영상으로 변환하고, 보다 좋은 영상을 얻기 위하여 곱 연산과 하이브리드 미디언 필터링을 이용하였다. 그리고 손 영역의 무게 중심을 구하여 이를 중심으로 손 영역의 움직임을 구하는 방법을 사용하였다. Pacman is known as the terminator of the loved classic game in the world. In this paper, classic pacman game without using a keyboard or a mouse we should be able to play the game with simple hand gestures. In other words, It use motion game to be a substitute for the hand direction key using the center coordinates. Also, movements of the monster is to be replaced depending on your hand movements by extracting a pointer of hand taking pictures in the MFC dialog using Cam. In this paper, YCbCbr image is convert to RGB images to extract skin color, and multiplication operations and hybrid median filtering was used in order to obtain better images. And it is used to obtain movement of the hand area based on obtain the center of gravity of the hand region.

Automatic machine embroidery image color analysis system. Part I: Using Gustafson-Kessel clustering algorithm in embroidery fabric color separation

This series of studies aims to propose the automatic machine embroidery image color analysis system to solve the problem of lack of manpower for machine embroidery fabric drafting and to shorten drafting time. The studies included three parts: (1) machine embroidery image color separation, (2) search of repeated pattern images, (3) machine embroidery color analysis system integration. This study aimed to find the optimal clustering algorithm and cluster validity indices for the automatic color separation process of machine embroidery fabric drafting in order to shorten drafting time. To improve image quality for computer analysis, this study used the color hybrid median filter to filter noise and the color bilateral filter to smoothen fabric and embroidery texture for subsequent color separation. By extracting the color a* component and b* component of the machine embroidery image in CIE L*a*b* color system, this study used the Gustafson-Kessel clustering algorithm for color separation. The Gustafson-Kessel clustering algorithm in machine embroidery image color separation can improve color separation accuracy, and its result is compared with that of the clustering algorithms commonly used in the color separation of color images. This study implemented the chromatography of the color separation results, and used the cluster validity indices to prove that the application of Gustafson-Kessel clustering algorithm in the machine embroidery image color separation system has better results than K-means, K-medoid, fuzzy C-means (FCM), and self-organizing map (SOM) clustering algorithm. The results meet the classifications as expected by human eyes.