Image Processing Filters Research Articles

Extended fragility surfaces for unreinforced masonry walls using vision-derived damage parameters

This paper introduces a novel methodology for identifying the post-earthquake damage states of unreinforced masonry walls using visual damage features. Current guidelines provide a qualitative description of surface cracking and crushing to determine the damage states. However, evaluating the damage states according to the qualitative damage description is highly affected by the experience and judgment of the inspector. Therefore, the present study quantifies the qualitative description of guidelines to measure the probability of reaching different damage states using 3D fragility surfaces. To this end, 333 images of 168 damaged unreinforced masonry walls at different drift ratios between 0.0 and 2.5 percent are collected, and the magnitude of cracking and crushing are measured. Three image processing filters are employed to measure various types of cracking and crushing areas, which are the main characteristics described by the guideline for damage state identification. Then, a new methodology for generating the 3D fragility surface, entitled the Box-counting method, is introduced to study the influence of cracking and crushing on the damage states. The fragility surfaces are developed using the information on crack pattern length and crushing areas that enable determining the damage states of the masonry walls just by looking at the surface damages. The Tree-based machine learning regression learners are also employed to formulate the 3D fragility surface. The proposed framework of this paper can be used for risk assessment of unreinforced masonry walls and is a reliable basis for generating multi-variable 3D fragility surfaces.

Encryption and enhance medical image using hybrid transform (Ã-module and partial fuzzy Ȟ-transform)

This research provides a novel application for fuzzy partial differential equations in the biomedical and engineering sectors. To create this program, we multiplied the Ã-module by the fuzzy partial F-transform, which yielded a new transform that was then used as a filter in image processing. Around 6 percent of all cancer cases in adults are caused by people under 20, and most cancers begin as acute interstitial pneumonia. In recent years, many oncologists have used mathematical models to draw conclusions and make forecasts. This is by experimental biological models. Based on the newly proposed rational valued cyclic group characters and partial fuzzy transformation, this study proposes a novel method for image processing of diseased cells, which involves solving such concepts as encoding and differentiating parameters, optimizing radiographic images, and converting the idea into a filter. Radiographic image optimization and parameter differentiation were also part of the study. It was shown that the suggested transformation provided much higher levels of intelligibility than the fuzzy partial Laplace transform analysis and categorization of how a patient’s illness trajectory affects their responsiveness to treatment.

QUANTITATIVE ANALYSIS OF CARBON BLACK AGGLOMERATES MORPHOLOGY IN ELASTOMER COMPOSITES BASED ON X-RAY TOMOGRAPHY BY MEANS OF NUMERICAL CLUSTERING

ABSTRACT A novel methodology for characterizing the morphology distribution of filler agglomerates in elastomer composites is presented based on laboratory-sourced X-ray tomography. Various feature extraction methods (via, e.g., image-processing filters, segmentation) and selection tools (Spearman's rank correlation coefficient) combined with K-means unsupervised clustering algorithm were developed for identifying the distinct morphological classes in model materials (carbon-filled ethylene propylene diene monomer rubber). The interest of this methodology was demonstrated by precisely differentiating the materials compounded with different processing parameters. For instance, in this example, thanks to this analysis, it was found that introducing the filler before the elastomer in internal mixer tends to favor more structured agglomerates.

An attempt at the possibility of using multi-output paraunitary filters for image processing for astrophotography trackers

The article deals with the issue of stabilizing the frame position of the shot in long time astrophotography. The authors described this issue from the point of view of guiding a telescope following of the sky, and also in terms of the possibility of supporting this process with digital signal processing using paraunitary filters. The procedure for obtaining a 2D paraunitary system from an initial (preset) filter is presented. An attempt is made to verify the data that can be obtained from individual outputs of paraunitary systems. Several example filters were shown and their operation was tested on a selected image of a real astronomical object from the telescope's guide camera. Based on the output images obtained (from three paraunit systems), the possibility of their further use in practice was evaluated.

Quantitative Nail Fold Capillary Blood Flow Using Capillaroscopy System and ImageJ Software in Healthy Individuals

Purpose: Measuring the blood flow velocity in capillaries is a useful method for diagnosing many diseases. Despite increasing interest in nailfold capillaroscopy, objective measures of capillary structure and blood flow have been rarely studied. This study aimed to measure the blood flow velocity along the capillary central line using capillaroscopy system, and also ImageJ software used Scale-Invariant Feature Transform (SIFT) tracking algorithms and Kalman filter for image processing.
 Materials and Methods: The Red Blood Cells (RBCs) velocity in capillaries of finger nailfold was measured in 12 normal cases using a novel capillaroscopy system. The measurements of the velocity were performed at 12 points in nailfold regions by two observers separately. The image processing and automated measurement take 1-2 min per nailfold. FFmpeg software was used to convert the images format, and then the images were imported to ImageJ software and segmented. SIFT tracking algorithms and Kalman filter were used to filter noise and irregularities in the images. For reproducibility, the velocity distribution values obtained by the two performers, and Paired T-Test was used. The reliability of a measuring instrument or calculation method depends on the tools obtained using Cronbach's alpha. To assess the repeatability of the algorithm, the capillary velocity values were executed at different times with 24-hour intervals using a coefficient of variance method.
 Results: The calculated RBCs velocity was in the range of 0.05-0.16 mm/s. The results based on Cronbach's alpha analysis for reliability factor was 0.97, with a good correlation among the measurements, 0.85. The average velocity (along with standard deviation) for repeatability at three different times was obtained 0.1195 ± 0.0246, 0.0974 ± 0.0221, and 0.0962 ± 0.0202 mm/s, demonstrating that there was no statistically variation between these measurements (P-value > 0.05). The velocity results for the two observers were 0.811 ± 0.392 and 0.819 ± 0.325 mm/s, indicating a good reproducibility between them (P-value = 0.959).
 Conclusion: For the measurements of nailfold capillaries velocity, there was good/reasonable reliability, repeatability, and reproducibility. The results indicated a good accuracy of capillaroscopy system and ImageJ software with SIFT algorithm and Kalman filter, which can be used as an appropriate tool for determining the rate of nailfold blood flow velocity.

Evaluation of the best Edge Filters in Image Processing Based on the Color Fabric Texture

With the development and complexity of life, the need to improve images appeared, especially when used in the fields of life, including industry and its branches, which affect the life of the citizen, such as the manufacture of fabrics. Which requires precision in the production of these fabrics from the colors and pattern of the fabric. Edge identification is the first step in many digital image-processing applications. Edge identification greatly decreases the data quantity, undesirable filters or unimportant data and provides the important data into the image. There are some issues such as false edge identification, noise issues, low contrast and other edge issues. This paper presents a practical study to compare different edge detectors to determine which edge detector achieves better results, which in turn reflects the best pattern in the fabric. These detectors are Canny, Roberts, Laplace and Gabor. A database of thirty color JPG images collected from the Internet was arranged and a quality scale was used to compare filter detectors. The system MATLAB2020 was used to program the proposed work. The results enhancement was measured by the quality coefficient. This coefficient estimated as follows for Roberts filter (44.27-51.09). Gabor filter (43.46-44.48) and Laplace filter (44.71-5.40). Finally, the quality coefficient for Canny filter equals (44.46-52.05). Therefore, it turns out that the Gabor filter is the best of these filters in defining the edges that were used in defining the pattern.

Machine learning-based seismic damage assessment of non-ductile RC beam-column joints using visual damage indices of surface crack patterns

After a significant earthquake, the updated status of the structural elements is usually determined based on a qualitative visual inspection. Although visual inspection provides a prompt assessment of the damaged elements, the output of this subjective method is influenced by the experience and decision of a trained inspector, which may vary from case to case. In this study, an innovative machine learning-based procedure is developed to automate damage state identification of non-ductile reinforced concrete moment frames (RCMFs) utilizing visual indices of crack patterns of the concrete surface. An extensive database including 264 surface crack patterns is constructed corresponding to 61 non-ductile beam-column joint specimens tested under the quasi-static cyclic loads at different drift ratios. Two visual damage indices, including the cumulative length of cracking and the areal density of crushing, are extracted using image processing filtering on the collected images. Supplementary information of the specimen, such as aspect ratio and concrete compressive force, is also considered as input variables. Various machine learning-based predictive models are developed for estimating the maximum drift ratio of a damaged non-ductile beam-column joint based on the available information of the specimen. Two scenarios are considered based on the accessibility of the compressive strength of the concrete as the input data, and a predictive model is obtained for each scenario. The peak experienced drift ratio predicted by the proposed methodology is finally used as an engineering demand parameter attributed to the corresponding fragility curves to determine the damage state of non-ductile RCMFs.

Severity Detection of Diabetic Retinopathy — A Review

Diabetic Retinopathy (DR) refers to a micro-vascular complication causing vision problems in diabetic patients. It is a serious global public health issue that damages the retinal blood vessels and leaves a large number of patients with loss of vision. Micro-aneurysms, hemorrhages, macular edema, and exudates are some of the lesions used to diagnose it. When detected early, Diabetic Retinopathy can be treated well with precise identification of lesions. Computer-aided diagnosis refers to the approaches used to extract specific features associated with the disease by using image processing filters. Some Machine Learning tools, especially the ones well-suited to data analysis applications are particularly useful. This paper primarily focuses on a comparative performance evaluation of many machine learning and deep learning-based techniques applied for the diagnosis of this vision ailment in diabetic patients.

The effect of image processing filters in detection of apical root resorption, interproximal dental decay, and vertical root fractures: A review

<h3>Background</h3> Digital radiography has been available in dentistry for over 35 years and its usage by dental practitioners is now a prerequisite for providing the standard of care. One advantage of digital radiography is the use of image processing filters (IPFs) to increase the diagnostic signals and decrease the non-diagnostic signals. For various diagnostic purposes, IPFs can be selected based on the practitioner's demand, experience, and decision. <h3>Objectives</h3> The purpose of this study was to evaluate published reports of the effectiveness of image processing filters (IPFs) of digital radiographs in detecting apical root resorption (ARR), interproximal dental decay (IDD), and vertical root fractures (VRF). <h3>Study design</h3> Original research presentations were selected by using the following inclusion criteria: (1) CMOS digital x-ray images, (2) IPFs built in to the MiPACS image management software, (3) Questionnaire studies, and (4) Studies that used human teeth. The search was limited to studies of Loma Linda University School of Dentistry Center for Dental Research. <h3>Results</h3> The search resulted in 3 studies fulfilling the above criteria, which evaluated IPFs in ARR, IDD, and VRF. ICC analysis showed excellent correlation in all studies. For ARR, the most accurate diagnoses as measured with the area under the curve (AUC) of receiver operating characteristic analysis was obtained with the Endo filter (AUC = .950; 95%CI: .869 - 1.0). The least accurate was the Dentin to Enamel filter (AUC = .758; 95%CI: .551 - .965). For IDD, the Edge Enhance filter was most accurate (AUC = 0.772; 95%CI: .669- .875) and the least was the Dentin to Enamel filter (AUC = 0.420; 95%CI: .287 - .553). For VRF, the most accurate diagnoses were achieved with the Contrast filter (AUC = 0.769; 95%CI: .687 - .851); the Endo filter was least accurate (AUC = 0.702; 95%CI: .612 - .792). <h3>Conclusions</h3> IPF can affect the diagnosis of ARR, IDD, and VRF depending on which type of filter is applied. A proper selection of filters is recommended to increase the possibility of the early diagnosis of pathology, thus reducing the need for further invasive dental treatment. <b>Statement of Ethical Review</b> Human/Animal subjects were used and this study was approved by an institutional ethics panel

High-speed recursive-separable image processing filters

The development of modern technologies in the field of image formation leads to an increase in the size of the generated images, as a result the question of reducing the processing computational costs arises, and this is an important factor in the creation of real-time systems. The study provides a description of high-speed recursive-separable filters for improving the quality of images, which, due to the peculiarities of their implementation, can reduce the number of computational operations required for the image processing process. This type of filters is obtained from two-dimensional linear digital filters, which are modified by applying recursive and separable properties to them. The MATLAB environment computing method for implementation of these filters is described. An extensive performance research of the developed filters has been carried out at various sizes of the test image and on various experimental installations. The comparison with the classical two-dimensional convolution method of the developed filters is demonstrated, and it shows the time gain required for the image processing. The results obtained can be applied in biomedical image processing systems or in vision systems working in heavy weather conditions.

Evaluation of resolution characteristics of digital intraoral radiographic images using a task transfer function.

This study aimed to quantitatively examine the effect of digital image processing of digital intraoral radiographic images on the resolution characteristics of the output image using a task transfer function (TTF). A photostimulable phosphor system with three types of image processing filters, including periodontal, endodontic, and dentine-to-enamel junction filters, was used. Each filter can be used in conjunction with the sharpness filter (+ S). Images were obtained from the original phantom, which combined aluminum disk and plate. The TTF, which indicates the resolution characteristics, was calculated. A one-dimensional profile curve was also measured, and the fluctuation in the pixel value was evaluated in detail. The results were compared to investigate the effects of digital image processing on digital intraoral radiographic images. The TTF values were specific to each filter. The change in the TTF strongly reflected the characteristics of the one-dimensional profile curve. The TTF was compared with a one-dimensional profile curve and was able to quantitatively express the resolution characteristics of all directions in the image. We attempted to evaluate the resolution characteristics of digital intraoral radiographic images with image processing filters using the TTF. The effect of each image processing filter and the + S filter on the resolution can be simply expressed using the TTF. Our results show that the TTF is useful for characterizing the resolution characteristics of image processing filters for image quality.

Formal analysis of 2D image processing filters using higher-order logic theorem proving

Two-dimensional (2D) image processing systems are concerned with the processing of the images represented as 2D arrays and are widely used in medicine, transportation and many other autonomous systems. The dynamics of these systems are generally modeled using 2D difference equations, which are mathematically analyzed using the 2D z-transform. It mainly involves a transformation of the difference equations-based models of these systems to their corresponding algebraic equations, mapping the 2D arrays (2D discrete-time signals) over the (z_1,z_2)-domain. Finally, these (z_1,z_2)-domain representations are used to analyze various properties of these systems, such as transfer function and stability. Conventional techniques, such as paper-and-pencil proof methods, and computer-based simulation techniques for analyzing these filters cannot assert the accuracy of the analysis due to their inherent limitations like human error proneness, limited computational resources and approximations of the mathematical expressions and results. In this paper, as a complimentary technique, we propose to use formal methods, higher-order logic (HOL) theorem proving, for formally analyzing the image processing filters. These methods can overcome the limitations of the conventional techniques and thus ascertain the accuracy of the analysis. In particular, we formalize the 2D z-transform based on the multivariate theories of calculus using the HOL Light theorem prover. Moreover, we formally analyze a generic (L_1,L_2)-order 2D infinite impulse response image processing filter. We illustrate the practical effectiveness of our proposed approach by formally analyzing a second-order image processing filter.

Improving cervical spinal cord lesion detection in multiple sclerosis using filtered fused proton density-T2 weighted images

BackgroundMagnetic Resonance Imaging (MRI) is considered a vital in depicting multiple sclerosis (MS) lesions. Current studies demonstrate that proton density (PD) weighted images (WI) are superior to T2 WI in detecting MS lesions (plaques) in the spinal cord.PurposeTo evaluate the diagnostic value of filtered fused PD/T2 weighted images in detecting cervical spinal cord MS lesions.Material and MethodsIn this retrospective study, we selected a sample size of 50 MS patients. Using contrast limited adaptive histogram equalization (CLAHE), a digital image processing filter was used on the (PD/T2) fused images. The produced images were inspected and compared to the original PD images by two experienced neuroradiologists using interobserver and intraobserver. An ROI analysis was also performed on the processed and original PD images.ResultsThe repeatability measurement of the match between the two examinations was highly consistent for both neuroradiologists. The repeatability for both neuroradiologists was 96.05%, and the error measurement was 3.95%. The reproducibility measurement of the neuroradiologist’s evaluation shows that the processed images could help to identify lesions better [excellent (84.87%)] than PD images [good (61.19%)]. ROIs analysis was performed on 113 MS lesions and normal areas in different images within the sample size. It revealed an enhanced ratio of 2.2 between MS lesions and normal spinal cord tissue in processed fused images compared to 1.34 in PD images.ConclusionThe processed images of the fused images (PD/T2) have superior diagnostic sensitivity for MS lesions in the cervical spine than PD images alone.

Generic conversion method for various spatial domain filters in quantum image processing

Quantum image processing deals with image processing applications in quantum computing. Features of quantum computing like superposition and parallelism give quantum algorithms superiority over classical algorithms. Image filters serve an important task in image processing for applying image transformations. In this paper, we proposed an algorithm for quantum image filters with a generic structure to support different filters. Pixel intensities are represented as rotation in Y-axis of Bloch sphere. The filter is implemented as addition or subtraction of the angles in Y-axis. The proposed algorithm is able to implement different image filters similar to a classical filter. We studied and presented the performance of the filter for edge detection and smoothing operation. The results are then compared to images from a classical filter. The algorithm is further compared with other existing QIP algorithms.

Towards Automatic Image Enhancement with Genetic Programming and Machine Learning

Image Enhancement (IE) is an image processing procedure in which the image’s original information is improved, highlighting specific features to ease post-processing analyses by a human or machine. State-of-the-art image enhancement pipelines apply solutions to fixed and static constraints to solve specific issues in isolation. In this work, an IE system for image marketing is proposed, more precisely, real estate marketing, where the objective is to enhance the commercial appeal of the images, while maintaining a level of realism and similarity with the original image. This work proposes a generic image enhancement pipeline that combines state-of-the-art image processing filters, Machine Learning methods, and Evolutionary approaches, such as Genetic Programming (GP), to create a dynamic framework for Image Enhancement. The GP-based system is trained to optimize 4 metrics: Neural Image Assessment (NIMA) technical and BRISQUE, which evaluate the technical quality of the images; and NIMA aesthetics and PhotoILike, that evaluate the commercial attractiveness. It is shown that the GP model was able to find the best image quality enhancement (0.97 NIMA Aesthetics), while maintaining a high level of similarity with the original images (Structural Similarity Index Measure (SSIM) of 0.88). The framework has better performance according to the image quality metrics than the off-the-shelf image enhancement tool and the framework’s isolated parts.

A Power-Efficient Multichannel Low-Pass Filter Based on the Cascaded Multiple Accumulate Finite Impulse Response (CMFIR) Structure for Digital Image Processing

The author offers a power-efficient multichannel low-pass filter for digital image processing based on the cascade multiple accumulate finite impulse response (CMFIR) structure in this study. The CMFIR filter was created using the outputs of a linear time-invariant system (LTI), which was built using a cascaded integrator comb (CIC) and a MAC low-pass filter. The sample rate convertor based on CIC filters effectively conducts decimation or interpolation. The sample rate convertor with the CIC filter can only accommodate narrowband transmissions and so cannot be utilized for wideband signals. The MAC architecture-based sample rate convertor is a good solution for high-bandwidth signals, but it uses more resources like registers and flip-flops, which increases power consumption. Here, the CMFIR low-pass filter acts as an interpolator, introducing a sample to boost the image's resolution. CMFIR is a useful tool for addressing the issue of aliasing during sampling. In addition, the genetic algorithm was used to increase the filter's resource utilization and power consumption efficiency.

Spatial analysis of damage evolution in cyclic-loaded reinforced concrete shear walls

This paper introduces a probabilistic framework to quantify the spatial distribution of cracking and crushing in rectangular reinforced concrete shear walls at different drift ratios. In this research, a comprehensive probabilistic spatial analysis is conducted on an extensive collected database of reinforced concrete shear walls tested under the quasi-static cyclic loading. The database includes 235 images of 72 damaged walls with various geometry and material properties at different drift ratios between 0.0 and 4.0%. Various image processing filters are implemented to the images to highlight the wall areas that are more prone to cracking and crushing. Then, advanced statistical analysis is carried out at the post-processing phase in order to quantify the spatial distribution of the damage. The results of the probabilistic analysis are presented for three major classes based on the variation of the wall aspect ratio. The damage heat maps are produced for each class, which show the concentration and severity of the occurred damages. In the following, statistical mixture models have been used to formulate the spatial variation of the damage over the 2D space of the concrete shear walls. A set of nonlinear predictive equations are also proposed to predict the probability of cracking at any specific zones of the walls based on the wall drift ratio. A major contribution of this study is to propose a visual benchmark for the inspectors to predict the peak-experienced drift ratio of a damaged wall (especially after a terminated cyclic-load) based only on the ultimate spatial distribution of damages. Therefore, the presented framework plays a crucial role in determining the post-hazard status of reinforced concrete shear walls based on the prediction of the peak drift ratio.

EYEDi: Graphical Authentication Scheme of Estimating Your Encodable Distorted Images to Prevent Screenshot Attacks

Graphical authentication schemes have the advantage of being more memorable than conventional passwords. Although some image distortion methods have been proposed to prevent the risks of over-the-shoulder attacks (OSAs), these methods cannot prevent camera recording attacks, as the key images are the same each time. In this study, we propose a graphical authentication scheme that generates various distorted images, named Estimating Your Encodable Distorted images (EYEDi). EYEDi generates distorted images by applying several image processing filters to the original images. Moreover, EYEDi estimates the appropriate image processing filter strength based on the authentication data. To measure attack resistance, twenty participants performed three types of attacks (OSA, camera recording attack, and screenshot) 300 times, each using existing methods and EYEDi. The classification error rate of all three types of attacks showed that EYEDi had a lower classification error rate between the legitimate user and attackers. Especially for the screenshot attack, which is the most severe threat model, the existing method was completely broken through, while EYEDi prevented the attacks with a classification error rate of 10&#x0025;. This result shows that EYEDi can eliminate the screenshot attacker by using the difference in authentication times and a simple improvement in defense performance.

Survey on Design of Digital FIR Filters using Optimization Models

As the discipline of Digital Signal Processing develops, digital filters play an increasingly vital role in modern technology (DSP). The FIR filter, which stands for finite impulse response, is the most common type of filter. As a result of its versatility, FIR filters find widespread application in many fields, including image filtering, frequency modulation, precision arithmetic, and many more. For this reason, digital FIR filters are designed using various optimization techniques. Using various optimization strategies yields the best results when optimizing for different filter coefficients (concerning control parameters, dependence, premature convergence, etc.). They're advantageous due to several factors, including their straightforward implementation, low error function, high-quality searching ability, and rapid convergence. In this paper, we have covered the topic of designing efficient digital filters for signal, image, and video processing using various optimization techniques.

Image Processing Filters for Grids of Cells Analogous to Filters Processing Grids of Pixels

Intra- and extra-cellular processes shape tissues together. For understanding how neighborhood relationships between cells play a role in this process, having image processing filters based on these relationships would be beneficial. Those operations are known and their application to microscopy image data typically requires programming skills. User-friendly general purpose tools for pursuing image processing on a level of neighboring cells were yet missing. In this manuscript I demonstrate image processing filters which process grids of cells on tissue level and the analogy to their better known counter parts processing grids of pixels. The tools are available as part of free and open source software in the ImageJ/Fiji and napari ecosystems and their application does not require any programming experience.