Median Filtering Method Research Articles

Image Processing in Improving the Scan Results of Identity Cards and Family Cards with Noise with the Median Filtering Method

In the digital era, identity documents such as Identity Cards and Family Cards play a crucial role in administration and public services. The process of scanning these documents often results in images with noise that can interfere with data accuracy and identification. This research aims to improve the quality of scanned images by using the median filtering method to reduce salt and pepper noise. Median Filtering was chosen for its ability to preserve edges and details in images, which is crucial for identity documents such as Identity Cards and Family Cards. In this study, the system developed to enhance scanned images was implemented and tested. Evaluation results show that the Median Filtering method is effective in removing noise without damaging the edges and fine details of the images. The evaluation indicates that this system is effective in producing images that meet the quality standards required for official administration and identification. This research is expected to contribute to the development of digital image enhancement technology for identity documents, thus improving accuracy and reliability in public services.

ViT-MAENB7: An innovative breast cancer diagnosis model from 3D mammograms using advanced segmentation and classification process

Tumors are an important health concern in modern times. Breast cancer is one of the most prevalent causes of death for women. Breast cancer is rapidly becoming the leading cause of mortality among women globally. Early detection of breast cancer allows patients to obtain appropriate therapy, increasing their probability of survival. The adoption of 3-Dimensional (3D) mammography for the medical identification of abnormalities in the breast reduced the number of deaths dramatically. Classification and accurate detection of lumps in the breast in 3D mammography is especially difficult due to factors such as inadequate contrast and normal fluctuations in tissue density. Several Computer-Aided Diagnosis (CAD) solutions are under development to help radiologists accurately classify abnormalities in the breast. In this paper, a breast cancer diagnosis model is implemented to detect breast cancer in cancer patients to prevent death rates. The 3D mammogram images are gathered from the internet. Then, the gathered images are given to the preprocessing phase. The preprocessing is done using a median filter and image scaling method. The purpose of the preprocessing phase is to enhance the quality of the images and remove any noise or artifacts that may interfere with the detection of abnormalities. The median filter helps to smooth out any irregularities in the images, while the image scaling method adjusts the size and resolution of the images for better analysis. Once the preprocessing is complete, the preprocessed image is given to the segmentation phase. The segmentation phase is crucial in medical image analysis as it helps to identify and separate different structures within the image, such as organs or tumors. This process involves dividing the preprocessed image into meaningful regions or segments based on intensity, color, texture, or other features. The segmentation process is done using Adaptive Thresholding with Region Growing Fusion Model (AT-RGFM)”. This model combines the advantages of both thresholding and region-growing techniques to accurately identify and delineate specific structures within the image. By utilizing AT-RGFM, the segmentation phase can effectively differentiate between different parts of the image, allowing for more precise analysis and diagnosis. It plays a vital role in the medical image analysis process, providing crucial insights for healthcare professionals. Here, the Modified Garter Snake Optimization Algorithm (MGSOA) is used to optimize the parameters. It helps to optimize parameters for accurately identifying and delineating specific structures within medical images and also helps healthcare professionals in providing more precise analysis and diagnosis, ultimately playing a vital role in the medical image analysis process. MGSOA enhances the segmentation phase by effectively differentiating between different parts of the image, leading to more accurate results. Then, the segmented image is fed into the detection phase. The tumor detection is performed by the Vision Transformer-based Multiscale Adaptive EfficientNetB7 (ViT-MAENB7) model. This model utilizes a combination of advanced algorithms and deep learning techniques to accurately identify and locate tumors within the segmented medical image. By incorporating a multiscale adaptive approach, the ViT-MAENB7 model can analyze the image at various levels of detail, improving the overall accuracy of tumor detection. This crucial step in the medical image analysis process allows healthcare professionals to make more informed decisions regarding patient treatment and care. Here, the created MGSOA algorithm is used to optimize the parameters for enhancing the performance of the model. The suggested breast cancer diagnosis performance is compared to conventional cancer diagnosis models and it showed high accuracy. The accuracy of the developed MGSOA-ViT-MAENB7 is 96.6 %, and others model like RNN, LSTM, EffNet, and ViT-MAENet given the accuracy to be 90.31 %, 92.79 %, 94.46 % and 94.75 %. The developed model's ability to analyze images at multiple scales, combined with the optimization provided by the MGSOA algorithm, results in a highly accurate and efficient system for detecting tumors in medical images. This cutting-edge technology not only improves the accuracy of diagnosis but also helps healthcare professionals tailor treatment plans to individual patients, ultimately leading to better outcomes. By outperforming traditional cancer diagnosis models, the proposed model is revolutionizing the field of medical imaging and setting a new standard for precision and effectiveness in healthcare.

Observations of Five Major Uranian Satellites during 2013–2020 Based on Gaia DR3

A total of 10,339 positions of the five major Uranian satellites (Ariel, Umbriel, Titania, Oberon and Miranda) were collected at the Yunnan Observatories from 2013 to 2020 with the 1.0 m astrometric reflector. A median filtering method was applied to reduce the influence of the bright Uranus halo. All CCD observational data was analyzed by the newly developed CCD image-processing software with Gaia DR3 as the reference catalog. The impact of different exposure times on satellite positioning accuracy was evaluated. The observational positions of satellites were compared with theoretical positions from Institut de Mécanique Céleste et de Calcul des Éphémérides (DE441 and Lainey et al. 2015) and the mean offsets are from −0.″115 to 0.″103 in both R.A. and decl. for the five satellites. The standard deviation of the offsets are from 0.″008 to 0.″086 for the four greatest Uranian satellites and from 0.″037 to 0.″132 for Miranda in each direction. These precise observations of the five major Uranian satellites over an 8 yr period will be very useful for improving the orbital parameters of both Uranus and its five major satellites.

Video Surveillance System-Based Human Activity Recognition Using Hierarchical Auto-Associative Polynomial Convolutional Neural Network with Garra Rufa Fish Optimization

Nowadays, video surveillance systems are gaining increasing attention in the computer vision fields due to user demands for security purposes. Observing human movement and predicting such movement perception are promising due to video surveillance systems. In this work, Hierarchical Auto-Associative Polynomial Convolutional Neural Networks (HA-PCNN) with Garran Rufa Fish Optimization (GRFO) is proposed for Human Activity Recognition (HAR) using a video surveillance system. Initially, video surveillance systems-based human activity images are obtained, which are collected by video camera by recording daily human activities. After that, the input images are fed to a pre-processing approach named as Switched Mode Fuzzy Median Filter (SMFM) method. In this, the noise presented in the input images is reduced by applying the SMFM model to normalize the dataset and improve the image quality. After that, the pre-processed images are given to the developed Fast Discrete Curvelet Transform with Wrapping (FDCT-WRP)-based feature extraction method to extract the relevant features. Moreover, the extracted features are given to the HA-PCNN model for HA classification. Generally, the HA-PCNN method does not express any adoption of optimization methods for scaling the ideal parameters and classification. Here, Garra Rufa Fish Optimization (GRFO) is proposed to enhance the parameters of HA-PCNN. Thus, the proposed HA-PCNN-GRFO methodology can classify human activities like standing, sitting, running, walking and sleeping. By then the performance of the proposed HA-PCNN-GRFO methodology is evaluated using Python Platform, and metrics like recall, accuracy, F-measure, precision, and specificity are analyzed. Thus, the proposed HA-PCNN-GRFO approach achieved 97.3% average accuracy rate, 96.5% average recall, 97.2% average precision, 95.9% average specificity, and 96.8% average F1-score for classifying human activities using UCI HAR dataset. Also, the proposed HA-PCNN-GRFO approach is outperformed in HAR than the conventional approaches.

A mixed domain deblending approach for simultaneous source data based on deep learning

The simultaneous source acquisition technology breaks the limitations of traditional seismic survey, which allows more than one source to fire almost at the same time. When the survey time is fixed, simultaneous source acquisition can increase the number of sources, while when the number of sources is fixed, this technique can greatly reduce the survey time. At present, the great advantages of this high-efficiency acquisition technology have received wide attention from academia and industry, and researchers have proposed a series of deblending methods and obtained good results. In recent years, the rapid development of deep learning provides a new solution for deblending, and it has obvious advantages in computational time compared to traditional methods when processing large-scale seismic data. We proposed a novel iterative deblending method based on deep learning, which integrates the advantages of seismic data processing in different domains. In the proposed method, by selecting the appropriate combination of domains, the separation quality is significantly improved compared to the deblended results in a single domain. The effectiveness of the proposed method is verified by deblending the synthetic and field data, and the better performance of the proposed method are demonstrated by comparing it with the multilevel median filter method and conventional deep learning-based methods.

Real-time concentration detection of Al dust using GRU-based Kalman filtering approach

Kalman filter algorithms have been widely used in dust environment concentration detection systems. However, in industrial environments, methods such as KF and median filtering usually require about 10 s of detection time, which cannot meet the requirements of online real-time detection. For this reason, this present study proposes a framework that combines a gated recirculation unit (GRU) with the KF method to achieve online real-time detection of dust concentration. In this framework, the GRU is mainly responsible for handling dynamic and nonlinear characteristics and capturing instantaneous concentration trends. On the other hand, the Kalman filter utilizes its superior state estimation capability to provide more accurate system state estimation by fusing real-time predictions from GRU and sensor measurements. The results show that the KFGRU method is superior to the conventional linear filtering method with a response time of less than 2 s and can detect dust concentration online in real-time. In terms of prediction accuracy, the deviation value of the curve processed by the KFGRU method is only 0.334, which is a significant breakthrough compared with the Kalman filter algorithm 0.755, the sliding average method 0.843, and the median filter method 0.849 (the smaller the deviation value, the higher the prediction accuracy). This study provides a comprehensive and innovative approach for dust concentration monitoring in dust reduction and explosion suppression systems, which not only meets the real-time requirement but also makes important progress in explosion safety management. This will provide more reliable and advanced technical support for dust control and safety in industrial production processes.

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.

Prototype Faraday Rotation Measure Catalogs from the Polarisation Sky Survey of the Universe’s Magnetism (POSSUM) Pilot Observations

The Polarisation Sky Survey of the Universe’s Magnetism (POSSUM) will conduct a sensitive ∼1 GHz radio polarization survey covering 20,000 deg2 of the southern sky with the Australian Square Kilometre Array Pathfinder. In anticipation of the full survey, we analyze pilot observations of low-band (800–1087 MHz), mid-band (1316–1439 MHz), and combined-band observations for an extragalactic field and a Galactic plane field (low-band only). Using the POSSUM processing pipeline, we produce prototype rotation measure (RM) catalogs that are filtered to construct prototype RM grids. We assess typical RM grid densities and RM uncertainties and their dependence on frequency, bandwidth, and Galactic latitude. We present a median filter method for separating foreground diffuse emission from background components and find that after application of the filter, 99.5% of the measured RMs of simulated sources are within 3σ of their true RM, with a typical loss of polarized intensity of 5% ± 5%. We find RM grid densities of 35.1, 30.6, 37.2, and 13.5 RMs per square degree and median uncertainties on RM measurements of 1.55, 12.82, 1.06, and 1.89 rad m−2 for the median-filtered low-band, mid-band, combined-band, and Galactic observations, respectively. We estimate that the full POSSUM survey will produce an RM catalog of ∼775,000 RMs with median-filtered low-band observations and ∼877,000 RMs with median-filtered combined-band observations. We construct a structure function from the Galactic RM catalog, which shows a break at 0.°7, corresponding to a physical scale of 12–24 pc for the nearest spiral arm.

Quality Control of Doppler Spectra from a Vertically Pointing, S-Band Profiling Radar

Abstract This study describes a novel combination of methods to remove spurious spectral peaks, or “spurs,” from Doppler spectra produced by a vertically pointing, S-band radar. The University of Massachusetts S-band frequency-modulated, continuous-wave radar (UMass FMCW) was deployed to monitor the growth of the CBL over northern Alabama during the VORTEX–Southeast field campaign in 2016. The Doppler spectra contained spurs caused by high-voltage switching power supplies in the traveling wave tube amplifier. In the original data-processing scheme for this radar, a median filtering method was used to eliminate most of the spurs, but the largest ones persisted, which significantly degraded the quality of derived radar moments (e.g., reflectivity, Doppler velocity, and spectrum width) and hindered further analysis of these data (e.g., hydrometeor classification and boundary layer height tracking). Our technique for removing the spurs consists of three steps: (i) a Laplacian filter identifies and masks peaks in the spectra that are characteristic of the spurs in shape and amplitude, (ii) an in-painting method then fills in the masked area based on surrounding data, and (iii) the moments data (e.g., reflectivity, Doppler velocity, and spectrum width) are then recomputed using a coherent power technique. This combination of techniques was more effective than the median filter at removing the largest spurs from the Doppler spectra and preserved more of the underlying Doppler spectral structure of the scatterers. Performance of both the median-filter and the in-painting methods is assessed through statistical analysis of the spectral power differences. Downstream products, such as boundary layer height detection, are more easily derived from the recomputed moments. Significance Statement This manuscript describes a novel combination of image and signal processing techniques used to recover meteorological observations from corrupted Doppler radar spectra. This successful recovery of meteorologically significant information illustrates the importance of retaining Doppler spectra when practical. In seeking solutions to data quality issues, the atmospheric science community should remain cognizant of promising techniques offered by other disciplines. We present this data rescue study as an example to the meteorological community.

Parametric image-based concrete defect assessment method

Structural health monitoring aims to ensure the integrity of infrastructure. Assessing structural integrity through image classification techniques based on human perception is often challenging. Incorporating a morphological image-based crack detection algorithm can mitigate these limitations. This study proposes a parametric approach for concrete crack analysis using digital image processing techniques. The objective is to detect damage to the concrete surface of infrastructures, potentially exacerbating structural deterioration. The research justifies the utilization of digital parametric image processing techniques, including a weighted median filter, grayscale, Otsu filter, and other methods. These techniques were applied to evaluate different concrete images for crack detection. The findings were validated through Image Quality Assessment (IQA), determining the condition of the studied images and statistical properties using metrics such as Mean Squared Error (MSE), Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), Blind/Referenceless Image Spatial Quality Evaluator (BRISQUE), Natural Image Quality Evaluator (NIQE), and Perceptual Image Quality Evaluator (PIQE). Additionally, uncontrollable factors such as variations in lighting effects under different conditions, testing setups, and different textures of the concrete were considered in this study. The comprehensive experimental results demonstrated that the Otsu filter outperforms other filtering techniques. Overall, the study achieved remarkable accuracy, approximately 95%, in detecting cracks on building infrastructure. The proposed method holds potential for integration with advanced techniques in practical applications for the maintenance and safety of infrastructure. This research not only contributes to technological advancements in concrete defect assessment but also carries significant implications for the future of automated and reliable structural health monitoring.

Global vision object detection using an improved Gaussian Mixture model based on contour.

Object detection plays an important role in the field of computer vision. The purpose of object detection is to identify the objects of interest in the image and determine their categories and positions. Object detection has many important applications in various fields. This article addresses the problems of unclear foreground contour in moving object detection and excessive noise points in the global vision, proposing an improved Gaussian mixture model for feature fusion. First, the RGB image was converted into the HSV space, and a mixed Gaussian background model was established. Next, the object area was obtained through background subtraction, residual interference in the foreground was removed using the median filtering method, and morphological processing was performed. Then, an improved Canny algorithm using an automatic threshold from the Otsu method was used to extract the overall object contour. Finally, feature fusion of edge contours and the foreground area was performed to obtain the final object contour. The experimental results show that this method improves the accuracy of the object contour and reduces noise in the object.

Adaptive enhancement method of irregular low-pixel architectural design image based on lightness component

ABSTRACT This study explores adaptive enhancement for irregular, low-pixel architectural design images, focusing on lightness components. Utilizing a median filter and wavelet threshold method removes image noise, followed by grayscale conversion in HSV space. Applying Retinex theory generates the initial enhanced image, further improved through wavelet transform image fusion technology, resulting in the final enhanced architectural design image. Experimental results demonstrate the method's effectiveness in adapting and enhancing irregular, low-pixel architectural design images, achieving notably improved outcomes. The proposed approach successfully extracts and utilizes valuable image information, enhancing overall image quality. This study found that the proposed method can effectively extract and better utilize useful information in images, achieving more ideal image enhancement effects.

Image Segmentation and Filtering of Anaerobic Lagoon Floating Cover in Digital Elevation Model and Orthomosaics Using Unsupervised k-Means Clustering for Scum Association Analysis

In various engineering applications, remote sensing images such as digital elevation models (DEMs) and orthomosaics provide a convenient means of generating 3D representations of physical assets, enabling the discovery of new insights and analyses. However, the presence of noise and artefacts, particularly unwanted natural features, poses significant challenges, and their removal requires the application of filtering techniques prior to conducting analysis. Unmanned aerial vehicle-based photogrammetry is used at Melbourne Water’s Western Treatment Plant as a cost-effective and efficient method of inspecting the floating covers on the anaerobic lagoons. The focus of interest is the elevation profile of the floating covers for these sewage-processing lagoons and its implications for sub-surface scum accumulation, which can compromise the structural integrity of the engineered assets. However, unwanted artefacts due to trapped rainwater, debris, dirt, and other irrelevant structures can significantly distort the elevation profile. In this study, a machine learning algorithm is utilised to group distinct features on the floating cover based on an image segmentation process. An unsupervised k-means clustering algorithm is employed, which operates on a stacked 4D array composed of the elevation of the DEM and the RGB channels of the associated orthomosaic. In the cluster validation process, seven cluster groups were considered optimal based on the Calinski–Harabasz criterion. Furthermore, by utilising the k-means method as a filtering technique, three clusters contain features related to the elevations associated with the floating cover membrane, collectively representing 84% of the asset, with each cluster contributing at least 19% of the asset. The artefact groups constitute less than 6% of the asset and exhibit significantly different features, colour characteristics, and statistical measurements from those of the membrane groups. The study found notable improvements using the k-means filtering method, including a 59.4% average reduction in outliers and a 36.3% decrease in standard deviation compared to raw data. Additionally, employing the proposed method in the scum hardness analysis improved correlation strength by 13.1%, removing approximately 16% of the artefacts in total assets, in contrast to a 3.6% improvement with the median filtering method. This improved imaging will lead to significant benefits when integrating imagery into deep learning models for structural health monitoring and asset performance.

A Signal Filtering Method for Magnetic Flux Leakage Detection of Rail Surface Defects Based on Minimum Entropy Deconvolution

Magnetic flux leakage (MFL) detection of rail surface defects is an important research field for railway traffic safety. Due to factors such as magnetization and material, it can generate background noise and reduce detection accuracy. To improve the detection signal strength and enhance the detection rate of more minor defects, a signal filtering method based on minimum entropy deconvolution is proposed to denoise. By using the objective function method, the optimal inverse filter parameters are calculated, which are applied to the filtering detection of MFL signals of the rail surface. The detection results show that the peak-to-peak ratio of the defect signal and noise signal detected by this algorithm is 2.01, which is about 1.5 times that of the wavelet transform method and median filtering method. The defect signal is significantly enhanced, and the detection rate of minor defects on the rail surface can be effectively improved.

Optimized Deep Learning Model for Flood Detection Using Satellite Images

The increasing amount of rain produces a number of issues in Kerala, particularly in urban regions where the drainage system is frequently unable to handle a significant amount of water in such a short duration. Meanwhile, standard flood detection results are inaccurate for complex phenomena and cannot handle enormous quantities of data. In order to overcome those drawbacks and enhance the outcomes of conventional flood detection models, deep learning techniques are extensively used in flood control. Therefore, a novel deep hybrid model for flood prediction (DHMFP) with a combined Harris hawks shuffled shepherd optimization (CHHSSO)-based training algorithm is introduced for flood prediction. Initially, the input satellite image is preprocessed by the median filtering method. Then the preprocessed image is segmented using the cubic chaotic map weighted based k-means clustering algorithm. After that, based on the segmented image, features like difference vegetation index (DVI), normalized difference vegetation index (NDVI), modified transformed vegetation index (MTVI), green vegetation index (GVI), and soil adjusted vegetation index (SAVI) are extracted. The features are subjected to a hybrid model for predicting floods based on the extracted feature set. The hybrid model includes models like CNN (convolutional neural network) and deep ResNet classifiers. Also, to enhance the prediction performance, the CNN and deep ResNet models are fine-tuned by selecting the optimal weights by the combined Harris hawks shuffled shepherd optimization (CHHSSO) algorithm during the training process. This hybrid approach decreases the number of errors while improving the efficacy of deep neural networks with additional neural layers. From the result study, it clearly shows that the proposed work has obtained sensitivity (93.48%), specificity (98.29%), accuracy (94.98%), false negative rate (0.02%), and false positive rate (0.02%) on analysis. Furthermore, the proposed DHMFP–CHHSSO displays better performances in terms of sensitivity (0.932), specificity (0.977), accuracy (0.952), false negative rate (0.0858), and false positive rate (0.036), respectively.

A deep learning-based load forecasting algorithm for energy consumption monitoring system using dimension expansion

As a basic task in energy consumption monitoring system, load forecasting has great effects on system operation safety, generation costs and economic benefits. In this paper, a long-term load forecasting algorithm using data dimension expansion and deep feature extraction is proposed. First, the outliers of the meteorological measurements are removed by median filter method, and then the time information is encoded to form the fingerprint of the training data. Next, the full connected network (FCN) is used to expand the dimensions of the fingerprint, and the convolutional neural network (CNN) is used to extract the deep features which can obtain better feature representation. Finally, the FCN, the CNN and regression learning model are combined for jointly offline training. The optimal parameters of these network can be obtained under global solution. Experimental results show that the proposed algorithm has better load forecasting performance than existing methods.

Comparison of Image Enhancement Methods for Diabetic Retinopathy Screening

The most common factor contributing to visual abnormalities that result in blindness is known as diabetic retinopathy (DR). Retinal fundus scanning, a non-invasive method that is integral to the picture pre-processing phase, can be used to identify and monitor DR. Low intensity, irregular lighting, and inhomogeneous color are some of the main issues with DR fundus photographs. Analysis of aberrant characteristics on retinal fundus images to identify diabetic retinopathy is one of the key responsibilities of image enhancement. However, a variety of approaches have been created and it is unknown whether one is best suited for use with images of the retinal fundus. This study investigated various image enhancement methods in order to see aberrant abnormalities on retinal fundus pictures more clearly. This study investigated various image enhancement methods in order to see aberrant abnormalities on retinal fundus pictures more clearly. The contrast-limited adaptive histogram equalization (CLAHE) method, the gray-level slicing method, the median filtering method, and the low light method are image improvement methods used to enhance images of the retinal fundus. The parameters Natural Image Quality Evaluator (NIQE), Mean Squared Error (MSE), Peak Signal-to-Noise Ratio (PSNR), and entropy will be used to assess each image enhancement technique's performance. An ophthalmologist from Sains University Hospital (HUSM) provided the image data. The findings indicate that while each technique has its own benefits, the CLAHE technique, with a standard deviation MSE of 0.0004, is the best.

Drought recognition based on feature extraction of multispectral images for the soybean canopy

Drought stress is a major environmental factor in soybean growth. Accurate and rapid detection of phenotypic traits during drought condition is important for water-saving irrigation and variety selection for the drought-resistant plant. The traditional manual measurement of phenotypes is time-consuming and error-prone, thus, an accurate and rapid drought recognition method was proposed based on feature extraction of multispectral images for the soybean canopy. First, the Suinong26 soybean variety was selected as the research object. The multispectral sensor (Parrot Drone SAS, Paris, France) was used to acquire the soybean canopies' multispectral image in four channels (NIR, RED, REG, and GRE). Second, after de-noising the original image using median filtering method, the target image of the soybean canopy in the NIR channel was extracted using a thresholding segmentation algorithm. Third, the segmented image was set as an image template to perform canopy segmentation on the rest of the channel images, the soybean canopy images were obtained in the RED, REG, and GRE channels. In addition, soybean canopies' 9 vegetation indexes (NDVI, GNDVI, NDGI, RVI, DVI, GCI, RECI, RDVI, NLI) and each channel's 7 texture features (mean, standard deviation, smoothness, third moment, information entropy, average gradient, fractal dimension) were calculated by using image fusion and processing technologies. Finally, based on the 37-dimensional features of the extracted multispectral images of the soybean canopy, a drought recognition model was established using a support vector machine (SVM) with a radial basis as the kernel function. The models' recognition accuracy reached 96.87%, and the running time was only 0.493441 s. The proposed model has improved the recognition accuracy by 8.33% on average compared with Genetic Algorithm-Backpropagation (GA-BP) neural network, radial basis neural network (RBF) and random forest (RF) method. The proposed method can provide a theoretical basis and technical support for precise regulation and scientific decision-making of breeding and cultivation management for drought-resistant soybean varieties.

Optimization of CBCT data with image processing methods and production with fused deposition modeling 3D printing.

The present study has investigated the effect of the removal of artifacts in cone beam computed tomography (CBCT) images with image processing techniques to dental implant planning. The aim of this study has been to benefit from the novel image processing techniques and additive manufacturing technologies in order to change the existing approach in the usage of the 3D model in the orthogonal surgery, traumatic cases, and tumor operations and to solve the restrictions in surgical operations. In the study, firstly, 3 × 3, 5 × 5, and 7 × 7 kernel values were determined on the CBCT image data of the patient. The determined kernel values were applied on CBCT images by choosing median, median-mean-Gaussian (MMG), and bilateral filters, which are quite successful in removing noise in medical images. A thresholding process to separate teeth and bones from soft tissue regions on CBCT images, histogram normalization for a balanced color distribution, morphology operations to reduce noise areas, and tooth and bone boundaries were determined as closely as possible to patient anatomy. The original image and the images obtained from image enhancement techniques were compared. Results showed that the 3 × 3 median filtering method from three different kernel values out of three different image processing methods used in the study greatly improved the artifacts. It has also been shown that the availability of image processing and additive manufacturing methods on CBCT images has been shown to be a highly important factor before dental surgery planning.

Detection and location of microaneurysms in fundus images based on improved YOLOv4 with IFCM

AbstractDiabetic retinopathy (DR) is one of the leading causes of blindness for people suffering from diabetes. Microaneurysm (MA) is the initial symptom of DR. MA is a lesion based disease which starts as small red spots on the retina and increases in size as the DR progresses which finally leads to blindness. So eliminating the lesion can effectively prevent DR at an early stage. However, due to complex retinal structure, different brightness and contrast of fundus images with effects of factors such as different patients, environment changes, and difference in acquisition equipment, it is difficult for existing detection algorithms to achieve accurate results of MA detection and location. Therefore, the detection algorithm of improved YOLOv4 (YOLOv4‐Pro) was proposed. First, an improved Fuzzy C‐Means (IFCM) clustering algorithm was proposed to optimize anchor parameters of target samples to improve matching results between anchors and feature graphs. In order to control noise and improve efficiency, a median filtering method was employed to update the criterion function of the original FCM algorithm, and a K‐means algorithm was employed to initialize clustering. Second, a SENet attention module was added in the backbone of YOLOv4 to enhance key information and suppress background, improving the confidence of MA effectively. Finally, the spatial pyramid pooling (SPP) module was added to the neck to enhance the acceptance domain of the output characteristics of the backbone network, and profits separating of important context information. The improved YOLOv4 with IFCM was verified on the Kaggle DR dataset and compared with other methods. Experimental results show that optimizing the prior frame with the IFCM algorithm can make it suitable to frame the Kaggle DR dataset, which improves the detection accuracy of the network by nearly 5%, and provides a nice performance on detection and location of MA in fundus images. This would help ophthalmologists finding the exact location of MA on retina, thereby simplifying the process and eliminating any manual intervention.