Adaptive Median Filter Research Articles

Segmentation and classification of brain tumor using Taylor fire hawk optimization enabled deep learning approach

ABSTRACT The brain is a crucial organ that controls the body’s neural system. The tumor develops and spreads across the brain as a result of irregular cell generation. The provision of substantial treatment to patients requires the early diagnosis of malignancies. However, timely diagnosis and accurate classification were difficult in the conventional models. Thus, the Taylor Fire Hawk optimization (TFHO) is implemented here for effective segmentation and classification. The TFHO is the merging of the Taylor series and Fire Hawk Optimizer (FHO). The de-noising is accomplished by the adaptive median filter, and the segmentation is carried out using M-Net, which has been trained by TFHO. Subsequently, image augmentation is performed to increase the image dimension, followed by the extraction of effective features. Finally, DenseNet is used for the classification, and the training is done by TFHO. The introduced method obtained 94.86% accuracy, 92.83% Negative Predictive Values, 89.33% Positive Predictive Values (PPV), 95.91% True Positive Rate (TPR), 4.37% False Negative Rate (FNR), and 90.98% F1-score.

Removal of Fix Magnitude Impulsive Noise (FMIN) Through Innovative Recursive MDBUTMF Procedure

This article proposes an innovative recursive modied decision based unsymmetrical trimmed median filter (RMDBUTMF) procedure for noisy overriding of digital photographs, which are eminently contaminated by FMIN. The proposed procedure reinstates the noisy photographical basis (which has magnitude at 0 or 255) by trimmed median magnitude (or the mean magnitude of all the free-noise photographical basis) in the computational photographical basis region under the recursive framework. The proposed procedure is experimented on distinctive digital photographs (Lena, Girl, Pepper and F16) on broad noise density and the proposed procedure reveals superior noisy-overridden photographs than the Mean Filter (MF), Median Filter (SMF), Adaptive Median Filter (AMF), Weight Median Filter (WMF), MDBUTMF in both Peak Signal-to-Noise Ratio (PSNR) and photographical quality.

Neural network recognition system for video transmitted through a binary symmetric channel

The demand for transmitting video data is increasing annually, necessitating the use of high-quality equipment for reception and processing. The paper presents a neural network recognition system for videos transmitted via a binary symmetrical channel. The presence of digital noise in the data makes it challenging to recognize objects in videos even with advanced neural networks. The proposed system consists of a noise interference detector, a noise purification system based on an adaptive median filter, and a neural network for recognition. The experiment results demonstrate that the proposed system effectively reduces video noise and accurately identifies multiple objects. This versatility makes the system applicable in various fields such as medicine, life safety, physics, and chemistry. The direction of further research may be to improve the model neural network, increasing the database for training or using other noises for modeling.

Performance comparison of decision-based median filtering for brain MR images with high multiplicative noise

Digital images, particularly medical images, are prone to various noise attacks, such as additive, multiplicative, etc. Salt and pepper noise (SPN) is a type of multiplicative noise that degrades image quality and complicates identification, potentially leading to inaccurate diagnoses. Removing SPN is crucial yet challenging as noise removal can also eliminate important edge details. This study addresses the challenge of removing SPN without losing edge details. Existing SPN filtering algorithms, such as Average Filter (AF), Median Filter (MF), Switched MF (SMF), Adaptive MF (AMF), and Decision-based MF (DMF), perform poorly under high SPN attacks. We propose two effective SPN filters, CA_SPN1 and CA_SPN2, under the framework of Cellular Automata (CA) and MF. CA_SPN1 excels at high SPN levels, while CA_SPN2, a combination of CA_SPN1 and AMF, is effective across all SPN levels. Quality evaluations using Image Quality Assessment (IQA) metrics – Peak Signal to Noise Ratio (PSNR), Structural Similarity Index (SSIM), Figure of Merit (FOM), and Perceptual Similarity (PSIM) – demonstrate that the proposed algorithms restore images more effectively while preserving edge details compared to state-of-the-art algorithms. Furthermore, experimental results confirm the superior restoration quality of the proposed filters, particularly under high SPN conditions.

Adaptive median filter salt and pepper noise suppression approach for common path coherent dispersion spectrometer

The Common-path Coherent-dispersion Spectrometer (CODES), an exoplanet detection instrument, executes high-precision Radial Velocity (RV) inversions by recording the phase shifts of interference fringes. Salt-and-pepper noise caused by factors such as improper operation of the CCD probe/analog-to-digital converter and strong dark currents may interfere with the phase information of the fringe. This lowers the quality of the interfering fringe image and significantly interferes with the RV’s inversion. In this study, an adaptive median filtering algorithm (CODESmF) based on submaximum and subminimum values is designed to eliminate the interference fringe image's salt-and-pepper noise as well as to reduce RV error. This allows the interference fringe image's phase information to be retained more completely. The algorithm consists of two major modules. Pixel Sub-extreme-based Filtered Noise Monitoring Module: discriminates signal pixels and noise pixels based on the submaximum and subminimum values of the pixels in the filtering window. Adaptive Median Filter Noise Suppression Module: the signal pixel is kept at the original value output, the noise pixel serves as the filtering window's center pixel, and the adaptive median filtering procedure is repeated numerous times with various filtering window sizes. According to the experimental findings, the CODESmF outperforms comparable algorithms and works better at recovering interference fringes. More than 90% of the phase/RV error caused by salt-and-pepper noise is typically eliminated by the CODESmF algorithm, and in certain circumstances, it can even remove roughly 98% of the phase error.

Hybrid Deep Optimal Network for Recognizing Emotions Using Facial Expressions at Real Time

Recognition of emotions by utilizing facial expressions is the progression of determining the various human facial emotions to infer the mental condition of the person. This recognition structure has been employed in several fields but more commonly applied in medical arena to determine psychological health problems. In this research work, a new hybrid model is projected using deep learning to recognize and classify facial expressions into seven emotions. Primarily, the facial image data is obtained from the datasets and subjected to pre-processing using adaptive median filter (AMF). Then, the features are extracted and facial emotions are classified through the improved VGG16+Aquila_BiLSTM (iVABL) deep optimal network. The proposed iVABL model provides accuracy of 95.63%, 96.61% and 95.58% on KDEF, JAFFE and Facial Expression Research Group 2D Database (FERG-DB) which is higher when compared to DCNN, DBN, Inception-V3, R-152 and Convolutional Bi-LSTM models. The iVABL model also takes less time to recognize the emotion from the facial image compared to the existing models.

An edge preserving median filter for images based on level-sets

We propose an edge preserving median filter, called the level-set adaptive median filter, for noise removal in images. This filter uses connected sets of pixels with the same value, namely level-sets, as flexible regions which contour to edges in the image. The filter determines whether a set is noise or signal and smooths the noise. These set regions are flexible in terms of shape since they are created based on their values, and being data-driven therefore provide the mechanism for the filter to preserve edges in the image. We used metrics such as Pratt's Figure of Merit and Peak-Signal-to-Noise Ratio on the labelled faces in the wild data set. We concluded that the proposed level-set adaptive median filter does remove noise while preserving the edges in the image better than the traditional adaptive median filter.

Towards COVID-19 detection and classification using optimal efficient Densenet model on chest X-ray images

COVID-19 detection is mainly based on molecular testing, especially polymerase chain reaction (PCR) analyses, to find the occurrence of the virus's genetic material in respiratory samples. Computed tomography (CT) scans and X-rays are the two chest imaging techniques, which may provide more details on lungs, aiding in the diagnosis. Radiological findings in COVID-19 cases typically involve special features such as bilateral involvement, ground-glass opacities, and consolidation of the lungs. Deep learning (DL) methods, particularly convolutional neural networks (CNNs) are used for analyzing COVID-19 infection. By training these techniques on large datasets of annotated chest X-rays (CXRs), researches have attained superior outcomes in precisely detecting the possible COVID-19 cases. Leveraging artificial intelligence together with radiological expertise holds great potential for facilitating prompt intervention in the ongoing global efforts and improving the efficiency of early detection. This article focuses on the design of mother optimization algorithm with DL-enabled detection and classification (MOA-DLEDC) technique for COVID-19 diagnosis using CXR images. In the MOA-DLEDC technique, the adaptive median filtering (AMF) approach is used to eliminate the existence of noise. In addition, the complex and intrinsic feature patterns are derived from the Efficient DenseNet model. Besides, the hyperparameter optimization of the Efficient DenseNet model takes place using MOA. For the COVID-19 detection process, the MOA-DLEDC technique applies sparse autoencoder (SAE) model. A detailed set of experiments was conducted on the CXR dataset to highlight the promising results of the MOA-DLEDC technique. The extensive results inferred that the used technique gains superior performance over other models with maximum accuracy of 99.59 %.

Enhancing early breast cancer diagnosis through automated microcalcification detection using an optimized ensemble deep learning framework

Background Breast cancer remains a pressing global health concern, necessitating accurate diagnostics for effective interventions. Deep learning models (AlexNet, ResNet-50, VGG16, GoogLeNet) show remarkable microcalcification identification (>90%). However, distinct architectures and methodologies pose challenges. We propose an ensemble model, merging unique perspectives, enhancing precision, and understanding critical factors for breast cancer intervention. Evaluation favors GoogleNet and ResNet-50, driving their selection for combined functionalities, ensuring improved precision, and dependability in microcalcification detection in clinical settings. Methods This study presents a comprehensive mammogram preprocessing framework using an optimized deep learning ensemble approach. The proposed framework begins with artifact removal using Otsu Segmentation and morphological operation. Subsequent steps include image resizing, adaptive median filtering, and deep convolutional neural network (D-CNN) development via transfer learning with ResNet-50 model. Hyperparameters are optimized, and ensemble optimization (AlexNet, GoogLeNet, VGG16, ResNet-50) are constructed to identify the localized area of microcalcification. Rigorous evaluation protocol validates the efficacy of individual models, culminating in the ensemble model demonstrating superior predictive accuracy. Results Based on our analysis, the proposed ensemble model exhibited exceptional performance in the classification of microcalcifications. This was evidenced by the model’s average confidence score, which indicated a high degree of dependability and certainty in differentiating these critical characteristics. The proposed model demonstrated a noteworthy average confidence level of 0.9305 in the classification of microcalcification, outperforming alternative models and providing substantial insights into the dependability of the model. The average confidence of the ensemble model in classifying normal cases was 0.8859, which strengthened the model’s consistent and dependable predictions. In addition, the ensemble models attained remarkably high performances in terms of accuracy, precision, recall, F1-score, and area under the curve (AUC). Conclusion The proposed model’s thorough dataset integration and focus on average confidence ratings within classes improve clinical diagnosis accuracy and effectiveness for breast cancer. This study introduces a novel methodology that takes advantage of an ensemble model and rigorous evaluation standards to substantially improve the accuracy and dependability of breast cancer diagnostics, specifically in the detection of microcalcifications.

Flow control method in concrete particles pneumatic sweeping process based on image recognition and OPC protocol

Based on image recognition and OPC(Object linking and embedding for Process Control) protocol, an intelligent pneumatic sweeping scheme is proposed to solve the problem of pavement aggregates cleaning. Firstly, the effects of four different auxiliary light sources and six different image segmentation methods on the recognition of single particle images were analyzed, and the maximum entropy segmentation method was the best under white light. Secondly, the effect of four different filtering methods on discrete multi-particle image recognition was studied. Among them, the adaptive median filter can better solve the problem of interference points misidentified as particles. The recognition effect of four different edge detection methods on aggregate particle pile was analyzed. And Canny operator with better effect was finally used to recognize particle pile from the overhead and side view. Finally, the communication mode between the host computer and the controller established based on OPC protocol was verified by experiments.

An enhanced Convolutional Neural Network (CNN) model for the detection of lung cancer using X-Ray image

Lung Cancer is a life-threatening disease which can be diagnosed by Medical Imaging such as X-Ray, MRI, CT Scan etc. This research presented an enhanced model using Convolutional Neural Network (CNN) to detect lung cancer using X-Ray image. Medical image processing relies heavily on the diagnosis of lung cancer images. It aids doctors in determining the correct diagnosis and management. For many patients, lung cancer ranks among the mostdeadly diseases. Many lives can be saved if cancerous growth is diagnosed early. The purported model was predominantly built on Convolutional Neural Network (CNN) architecture and the model was built with enhanced features such as Image Enhancement, Segmenting ROI (Region of Interest), Features Extraction and Nodule Classification. In preprocessing stage, the AMF (Adaptive Median Filter) filtering method was applied to eliminate noise in X-Ray image of the dataset, and quality of X-Ray image was improved with the support of CLAHE (Contrast Limited Adaptive Histogram Equalization). Secondly, K-means Clustering algorithm was used to extract the relevant feature or Region of Interest (ROI) of the lung field automatically i.e. the model was effectively trained to identify and crop the exact location of the lung field automatically. The model was able to classify the cancer nodule as either Cancerous or Non-Cancerous. The framework worked on C# platform, and used EMGU for detection of the tumour in the lung xray image. Experimental result showed that the developed system was able to detect Lung Cancer with 90.77% accuracy, 86.65% precision and 95.31% Recall/Sensitivity.

Sine hunter prey optimization enabled deep residual network for diabetes mellitus detection using tongue image

Background: Many people suffer from Diabetes Mellitus (DM), a disease caused by high blood glucose levels. In real-time, many methods are implemented to diagnose DM to obtain a good accuracy level, but those methods remain costlier. Objective: To develop a method for DM detection with good accuracy and minimum cost. Materials and methods: In this research, DM is detected using tongue image based on DL model, named Deep Residual Network (DRN) that is trained by proposed Sine Hunter Prey Optimization (SHPO). Here, an adaptive median filter is used for the pre-processing phase, and image segmentation is done using ResUNet++, which is trained by Exponential Anti Corona Virus Optimization (ExpACVO). Here, ExpACVO integrates Anti Corona Virus Optimization (ACVO) and Exponential Weighted Moving Average (EWMA). Further, image augmentation and appropriate feature extraction stages are carried out, leading to DM detection by DRN. Moreover, SHPO is formed by combining the Sine Cosine Algorithm (SCA) and Hunter Prey Optimization (HPO). The performance of the proposed method is analyzed using the Tongue image dataset and the Diabetic images dataset. Results: The performance of SHPO_DRN is found using four evaluation metrics: accuracy, sensitivity, specificity, and f-measure. Here, these metrics exhibit superior performance with high-range values of 0.961, 0.970, 0.948, and 0.961. Conclusion: The proposed method detects the DM at earlier stages with a good accuracy.

Cutting through the noise: A Three-Way Comparison of Median, Adaptive Median, and Non-Local Means Filter for MRI Images

Medical Imaging is an essential practice in radiology to create high-standard images of the human brain. In medical imaging, denoising techniques are essential during image processing for a meaningful view of the anatomical structure of the images. In order to overcome the denoising issues, various filtering techniques and smoothening algorithms have come forth to get an accurate image for better diagnosis while preserving the original image quality. This work utilizes three computational methods for filtering noise that could distort the factual information in MRI images. The input used as the data throughout this study are MR images in grayscale contaminated with Salt and pepper noise, the most common noise in MRI images. To de-noise, a comparative analysis of three specific filters, namely the Non-Local Means filter, Median filter, and Adaptive Median filter, is conducted to do a study that gives the best results among them at different noise densities. Peak Signal-To-Noise Ratio (PSNR) and Structural Similarity Index (SSIM) are utilized as the main components to examine the behavior of the suggested filters in this study. The results show that at every value of noise density, i.e., 0.1, 0.3, 0.6, the adaptive median filter gives the highest average PSNR of 42.04, 34.36, and 28.10 and average SSIM of 0.97, 0.95, and 0.91, respectively. Hence, it indicates that the adaptive median filter outperforms the other two filters regarding PSNR and SSIM.

Segmentation and Volumetric Analysis of Heart from Cardiac CT Images.

Cardiac CT is a valuable diagnostic tool in evaluating cardiovascular diseases. Accurate segmentation of the heart and its structures from cardiac CT and MRI images is essential for diagnosing functional abnormalities, treatment plans and cardiovascular diseases management. Accurate segmentation and quantitative assessments are still a challenge. Manual delineation of the heart from the scan images is labour-intensive, time-consuming, and error prone as it depends on the radiologist's experience. Thus, automated techniques are highly desirable as they can significantly improve the efficiency and accuracy of image analysis. This work addresses the above problems. A new, image-driven, fast, and fully automatic segmentation method was developed to segment the heart from CT images using a processing pipeline of adaptive median filter, multi-level thresholding, active contours, mathematical morphology, and the knowledge of human anatomy to delineate the regions of interest. The algorithm proposed is simple to implement and validate and requires no human intervention. The method is tested on the 'Image CHD' DICOM images (multi-centre, clinically approved single-phase de-identified images), and the results obtained were validated against the ground truths provided with the dataset. The results show an average Dice score, Jaccard score, and Hausdorff distance of 0.866, 0.776, and 33.29mm, respectively, for the segmentation of the heart's chambers, aorta, and blood vessels. The results and the ground truths were compared using Bland-Altmon plots. The heart was correctly segmented from the CT images using the proposed method. Further this segmentation technique can be used to develop AI based solutions for segmentation.

An Underwater Localization Method Based on Visual SLAM for the Near-Bottom Environment

The feature matching of the near-bottom visual SLAM is influenced by underwater raised sediments, resulting in tracking loss. In this paper, the novel visual SLAM system is proposed in the underwater raised sediments environment. The underwater images are firstly classified based on the color recognition method by adding the weights of pixel location to reduce the interference of similar colors on the seabed. The improved adaptive median filter method is proposed to filter the classified images by using the mean value of the filter window border as the discriminant condition to retain the original features of the image. The filtered images are finally processed by the tracking module to obtain the trajectory of underwater vehicles and the seafloor maps. The datasets of seamount areas captured in the western Pacific Ocean are processed by the improved visual SLAM system. The keyframes, mapping points, and feature point matching pairs extracted from the improved visual SLAM system are improved by 5.2%, 11.2%, and 4.5% compared with that of the ORB-SLAM3 system, respectively. The improved visual SLAM system has the advantage of robustness to dynamic disturbances, which is of practical application in underwater vehicles operated in near-bottom areas such as seamounts and nodules.

Lidar signal processing method for atmospheric coherence length measurement based on the WD-ADMF

A denoising method applied to atmospheric coherent length lidar is proposed. Wavelet decomposition (WD) and the adaptive median filter (ADMF) are combined in this method. In this research, the effectiveness of the WD-ADMF has been verified through simulation and measurement. The results show that this filter algorithm, when applied to lidar data, improves the average peak signal-to-noise ratio (PSNR) and centroid error while maintaining data integrity such that the measurement of coherence length or the inference of C n 2 from coherence length more closely matches simulated truth and measured data.

A comprehensive achievement investigation of iterative mean filter for outlier extinguish aspiration on ubiquitous FVIN

Under commonwealth of the outlier extinguish inspection, exclusively on the impulsive outlier, the outlier extinguish algorithm is a substantial step, which is early performed prior to further computer vision steps thereupon the iterative mean filter (IMF) is inaugurated for fix value impulsive noise (FVIN) and grown into one of the superior achievement outliers extinguish algorithms. This academic article focuses to investigate the correlative achievement of the outlier extinguish algorithm established on IMF, is inaugurated from mean filter (MF) for carrying out the poor achievement of the aforesaid outlier extinguish algorithms (standard median filter (SMF), MF, and adaptive median filter (AMF)), for FVIN at omnipresent scattering of outlier consistency (5-90%). The analytical experiment comprehensively exploits on bountiful figures (F16, Girl, Lena, and Pepper) that are inspected in order to analyze the correlative achievement of an outlier extinguish algorithm established on IMF. In contrast with the aforesaid outlier extinguish algorithms (SMF, MF, and AMF), the outlier extinguish algorithm established on IMF has superior achievement from the experimental results.

Portable Structure Surface Crack Detection System Based on Android Platform

Cracks, potholes, and other defects often occur on infrastructure such as bridges, among which cracks are one of the most frequent defects. They have diverse shapes and are difficult to detect. Traditional manual inspection methods are inefficient and have low accuracy, while automated inspection machines are bulky and inconvenient to carry and use. Based on the shortcomings of existing detection technologies, this paper proposes a portable structural surface crack detection system based on the Android platform using a portable handheld image acquisition device. The system captures cracks on the structure's surface and obtains high-definition crack images. Then, these images are transmitted to portable smartphone terminals through Wi-Fi. Next, the image is pre-processed using weighted averaging, grayscale linear transformation, and adaptive median filtering. Then, the improved Canny edge detection algorithm is applied to identify crack information, and the edge segmentation algorithm is used to determine the crack width. Finally, based on camera calibration, the pixels are converted into the length data required for actual measurement. The results show that the system is easy to operate, and it not only has crack storage and tracking functions, but also can effectively measure the crack width on the surface of components. The measurement accuracy of this system reaches the sub-pixel level, and in actual testing, compared with the crack width gauge, the maximum relative error does not exceed 6.25%.

A Method for Electric Vehicle Charging Port Recognition in Complex Environments based on Wavelet Enhancement and Improved Canny Algorithm

Background: The current method has low detection accuracy for electric vehicle charging ports in complex environments. background: The current method has low detection accuracy for electric vehicle charging ports in complex environments. Objective: This paper proposes a method for electric vehicle charging port recognition based on wavelet enhancement and an improved canny algorithm. objective: This paper proposes a method for electric vehicle charging port recognition based on wavelet enhancement and improved canny algorithm. Methods: Firstly, a preprocessing assessment model is proposed to determine whether preprocessing enhancement should be applied. Subsequently, we perform a stretch transformation on the S channel in the HSI color space and apply a discrete wavelet transform to the I channel to restore the brightness and detail features of the target objects in the image. Specifically, in this paper, adaptive median filtering and multi-scale Retinex algorithm are utilized on different frequency components respectively. Finally, the features and parameters of the charging port are detected and extracted through the maximum inter-class variance method, morphological processing, the improved Canny algorithm, and Hough transform fitting. Results: This paper conducts comparative experiments on preprocessing enhancement of charging port images in different environments and features detection of charging ports. The hole feature parameters detected by this method are consistent with the actual situation. Conclusion: The experiment proves that the method proposed in this paper can meet the requirements of precise identification of charging ports in complex environments.

Image Analysis and Classification Using HRSVM-CNN for Land-Cover Classification by Using Remote Sensing Images

Objective: To effectively analyze diverse satellite images and derive valuable insights, it's crucial to employ efficient methods for classification and image processing. However, due to imperfections in image formats and sensor data, satellite imagery often contains flaws and inaccuracies, requiring extra steps to enhance its quality. The proposed solution involves two key techniques: segmenting the input image using a Hybrid HRSVM-CNN and classifying the resulting high-resolution remote sensing image using a Convolutional Neural Network. This combined approach addresses the challenges posed by image inconsistencies and aims to improve the accuracy and efficiency of current methods for satellite image analysis. Methods: In this research, a high-resolution Support Vector Machine-Convolutional Neural Network (Hybrid HRSVM-CNN) and texture characteristics are used to create an automated land identification method for satellite Remote sensing (RS) images. This approach's main focus is segmentation using the Bendlet Transform and Improved Chan-Vese, and it also does classification using a Hybrid HRSVM-CNN based on feature extraction and gray-level co-occurrence matrix algorithm. Findings: The proposed classification method's accuracy was evaluated against several other classification algorithms, including Semi-Supervised Graph Based Method (SSG), Conditional Random Fields (CRF), k-Nearest Neighbor (KNN), and Bi-layer Graph-based Learning (BLGL), Support Vector Machine (SVM), and Artificial Neural Networks (ANN). When compared with existing methods, the findings of the proposed method display excellent accuracy of 98.83%. Novelty: To start, an adaptive median filter is used to pre-process the satellite Remote sensing images, removing unwanted noises and other impacts. Following pre-processing, the image is segmented using the Bendlet Transform and Improved Chan-Vese algorithms. Gray-level co-occurrence matrix is utilized to extract texture information, and the Hybrid HRSVM-CNN is then used to categorize the various types of land. These applications frequently have a variety of issues that affect the categorization accuracy. A few significant factors, like location, irregularity, form, and diameter, reduce the process's overall accuracy. This research focuses on presenting a unique Land-Cover classification model in order to address such problems. The UC Merced Land Use dataset was considered in this research. Keywords: Bendlet Transform and Improved Chan­Vese Segmentation, Hybrid HRSVM­CNN, Land detection system, Satellite remote sensing images, Adaptive median filter, Gray­ level co­occurrence matrix