Fuzzy Edge Detection Research Articles

Retraction Note: Novel fuzzy logic expert system-based edge detection for X-ray images

Retraction Note: Novel fuzzy logic expert system-based edge detection for X-ray images

ROI extraction in corona virus (COVID 19) CT images using intuitionistic fuzzy edge detection

Edge detection is a vital aspect of medical image processing, playing a key role in delineating borders and contours within images. This capability is instrumental for various applications, including segmentation, feature extraction, and diagnostic procedures in the realm of medical imaging. COVID-19 is a deadly disease affecting people in most of countries in the world. COVID-19 is due to the coronavirus which belongs to the family of RNA viruses and causes various symptoms such as pneumonia, fever, breathing difficulty, and lung infection. ROI extraction plays a vital role in disease diagnosis and therapeutic treatment. CT scans can help detect abnormalities in the lungs that are characteristic of COVID-19, such as ground-glass opacities and consolidation. This research work proposes an Intuitionistic fuzzy (IF) edge detector for the segmentation of COVID-19 CT images. Intuitionistic fuzzy sets go beyond conventional fuzzy sets by incorporating an additional parameter, referred to as the hesitation degree or non-membership degree. This extra parameter enhances the ability to represent uncertainty more intricately in expressing the degree to which an element may or may not belong to a set. The IF edge detector generates proficient results, when compared with the traditional edge detection algorithms and is validated in terms of performance metrics for benchmark images. Intuitionistic fuzzy edge detection has been shown to be effective in handling uncertainty and imprecision in edge detection.

Klasik ve Bulanık Kenar Algılama Yöntemlerinin Karşılaştırılması

Edge detection is one of the challenging problems in image processing. Four different classical edge detection methods—Sobel, Prewitt, Roberts, and Canny—and type-1 and type-2 fuzzy logic-based edge detection methods are applied to analyze two separate datasets with various properties. The datasets are STARE which contains medical images of the retina and BIPED which contains images of the street. Furthermore, two separate hybrid fuzzy logic methods are implemented. The type-1 and type-2 fuzzy inference techniques are combined to produce the hybrid-1 and hybrid-2 approaches, using the "AND" and "OR" logic operators. We compare the simulation results for each technique using three different image quality metrics. These are Mean Square Error (MSE), Peak Signal Noise Ratio (PSNR), and Structural Similarity Index (SSIM). The type-2 fuzzy technique outperformed the hybrid-1 fuzzy method in visual quality metrics comparison, demonstrating superior blood vessel recognition on the STARE retinal image dataset—a dataset that more closely resembles the human visual system. Using the BIPED street image dataset, the hybrid-1 fuzzy approach outperformed the Roberts method. The hybrid-1 fuzzy technique showed good results in the second order for both kinds of datasets. Any data and general applications can take advantage of it.

Improved COVID‐19 Diagnosis Using a Hybrid Transfer Learning Model with Fuzzy Edge Detection on CT Scan Images

Improved COVID‐19 Diagnosis Using a Hybrid Transfer Learning Model with Fuzzy Edge Detection on CT Scan Images

Notice of Violation of IEEE Publication Principles: A Gravitational Search based Fuzzy Approach for Edge Detection in Colour And Grayscale Images

Notice of Violation of IEEE Publication Principles A Gravitational Search based Fuzzy Approach for Edge Detection in Colour And Grayscale by Swagatam Das, Satrajit Mukherjee, Bodhisattwa Prasad Majumder and Aritran Piplai Submitted to IEEE Transactions on Fuzzy System After careful and considered review of the content and authorship of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE's Publication Principles. This paper duplicates significant amounts of content from the original paper cited below. The original text was copied without attribution (including appropriate references to the original author(s) and/or paper title) and without permission. Due to the nature of this violation, reasonable effort should be made to remove all past references to this paper, and future references should be made to the following article: An Optimal Fuzzy System for Edge Detection in Color Images using Bacterial Foraging Algorithm, by Om Verma Submitted to IEEE Transactions on Evolutionary Computation, 12 April 2012 This article presents an optimal edge detection scheme based on the concepts of fuzzy Smallest Univalue Assimilating Nucleus (SUSAN) and the Gravitational Search Algorithm (GSA). Initially, the Univalue Assimilating Nucleus (USAN) area is calculated from the gray levels of every neighborhood pixel of a pixel of interest in the test image. In accordance with the SUSAN principle, the neighborhood is chosen as a circular mask and applied separately on the individual RGB components of the image in case the image is a color image. The USAN area edge map of each component is fuzzified using a Gaussian membership function (used for detecting strong edges) and a bell-shaped function (used for detecting weak edges). Then the entropy and edge sharpness factors are calculated from these fuzzy measures and optimized using GSA by evolving the fuzzifier and the parameters controlling the shape and range of the bell-shaped curve. Adaptive thresholding converts the fuzzy domain edge map to a spatial domain edge map. Finally, the individual RGB edge maps are concatenated to obtain the final image edge map. Qualitative and quantitative comparisons have been rendered with respect to a few promising edge detectors (both traditional as well as state-of-the-art) and also optimal fuzzy edge detectors based on metaheuristic algorithms like Differential Evolution (DE) and Particle Swarm Optimizer (PSO). Extensive comparisons based on several quantitative measures strongly reflect merits of the proposed method.

Fuzzy edge detection method of product packaging image based on Kalman filter

Fuzzy edge detection method of product packaging image based on Kalman filter

ED algorithm of inscription picture combining fuzzy logic rules

The expansive growth of information on the Internet has led to new developments in computer vision technology and image processing techniques. Since stone inscriptions are subject to erosion and polishing by the external environment for years, it is difficult to extract image and text information. In this study, the fuzzy control theory is combined with edge detection technology for image edge detection. Firstly, a suitable fuzzy rule and affiliation function are set, then a fuzzy control system is used to extract and detect the image edge information, and then a fuzzy logic rule-based edge detection algorithm is proposed to detect the inscription images. To test the performance of the algorithm, the detection effect of the image is first analyzed from a subjective perspective. The experimental results show that the proposed algorithm has better edge detection for both inscription and lena images, with better noise suppression without excessive distortion, and clearer inscription images. The proposed algorithm has the lowest MSE value of 41.26 when the detection object is the lena image b, and the highest PSNR value of 33.84 when the detection object is the lena image a. The proposed algorithm has the lowest MSE value of 41.26 when the detection object is the lena image b, and the highest PSNR value of 41.26 when the detection object is the lena image b. The proposed algorithm has the highest PSNR value of 33.84 when the detection object is the lena image b. In summary, the analysis of both subjective and objective indicators shows that the inscription image processing algorithm used in this paper has better processing effect, and the processed images become clearer with less distortion, which is helpful for both inscription image and text extraction.

RETRACTED ARTICLE: Novel fuzzy logic expert system-based edge detection for X-ray images

RETRACTED ARTICLE: Novel fuzzy logic expert system-based edge detection for X-ray images

Graded fuzzy edge detection for imperceptibility optimization of image steganography

ABSTRACT The edge area of an image is more resilient to distortion caused by embedding in steganography, driving the advancement of edge detection-based methods. State-of-the-art techniques, including hybrid, dilation, and Fuzzy-based approaches, have been developed to enhance steganography performance. Typically, these methods categorize image pixels into two regions: edge and non-edge areas. This research introduces graded Fuzzy edge detection, classifying pixels into main, supporting, and non-edge categories. Consequently, the embedding priority and message bit determination become more diverse and adaptive. Experimental results demonstrate that graded Fuzzy edge detection optimizes imperceptibility while preserving security and capacity. This technique is combined with various steganographic and reversible data hiding (RDH) methods, exhibiting improvements in stego image quality with over 1dB enhancement based on PSNR, as well as maintaining payload capacity and security aspects according to RS and PDH steganalysis.

A point base appraisal of fuzzy edge detection techniques in computer vision

A point base appraisal of fuzzy edge detection techniques in computer vision

A Fast Circle Detection Algorithm Based on Circular Arc Feature Screening

Circle detection is a crucial problem in computer vision and pattern recognition. In this paper, we propose a fast circle detection algorithm based on circular arc feature screening. In order to solve the invalid sampling and time consumption of the traditional circle detection algorithms, we improve the fuzzy inference edge detection algorithm by adding main contour edge screening, edge refinement, and arc-like determination to enhance edge positioning accuracy and remove unnecessary contour edges. Then, we strengthen the arc features with step-wise sampling on two feature matrices and set auxiliary points for defective circles. Finally, we built a square verification support region to further find the true circle with the complete circle and defective circle constraints. Extensive experiments were conducted on complex images, including defective, blurred-edge, and interfering images from four diverse datasets (three publicly available and one we built). The experimental results show that our method can remove up to 89.03% of invalid edge points by arc feature filtering and is superior to RHT, RCD, Jiang, Wang, and CACD in terms of speed, accuracy, and robustness.

Fuzzy edge detection method of product packaging image based on Kalman filter

Fuzzy edge detection method of product packaging image based on Kalman filter

Intelligent Machine Learning Enabled Retinal Blood Vessel Segmentation and Classification

Automated segmentation of blood vessels in retinal fundus images is essential for medical image analysis. The segmentation of retinal vessels is assumed to be essential to the progress of the decision support system for initial analysis and treatment of retinal disease. This article develops a new Grasshopper Optimization with Fuzzy Edge Detection based Retinal Blood Vessel Segmentation and Classification (GOFED-RBVSC) model. The proposed GOFED-RBVSC model initially employs contrast enhancement process. Besides, GOAFED approach is employed to detect the edges in the retinal fundus images in which the use of GOA adjusts the membership functions. The ORB (Oriented FAST and Rotated BRIEF) feature extractor is exploited to generate feature vectors. Finally, Improved Conditional Variational Auto Encoder (ICAVE) is utilized for retinal image classification, shows the novelty of the work. The performance validation of the GOFED-RBVSC model is tested using benchmark dataset, and the comparative study highlighted the betterment of the GOFED-RBVSC model over the recent approaches.

Numerical upscaling of parametric microstructures in a possibilistic uncertainty framework with tensor trains

A fuzzy arithmetic framework for the efficient possibilistic propagation of shape uncertainties based on a novel fuzzy edge detection method is introduced. The shape uncertainties stem from a blurred image that encodes the distribution of two phases in a composite material. The proposed framework employs computational homogenisation to upscale the shape uncertainty to a effective material with fuzzy material properties. For this, many samples of a linear elasticity problem have to be computed, which is significantly sped up by a highly accurate low-rank tensor surrogate. To ensure the continuity of the underlying mapping from shape parametrisation to the upscaled material behaviour, a diffeomorphism is constructed by generating an appropriate family of meshes via transformation of a reference mesh. The shape uncertainty is then propagated to measure the distance of the upscaled material to the isotropic and orthotropic material class. Finally, the fuzzy effective material is used to compute bounds for the average displacement of a non-homogenized material with uncertain star-shaped inclusion shapes.

Fuzzy Logic Approaches in the Task of Object Edge Detection

The task of reducing the computational complexity of contour detection in images is considered in the article. The solution to the task is achieved by modifying the Canny detector and reducing the number of passes through the original image. In the first case, two passes are excluded when determining the adjacency of the central pixel with eight adjacent ones in a frame of size 3х3. In the second case, three passes are excluded, two as in the first case and the third one necessary to determine the angle of gradient direction. This passage is provided by a combination of fuzzy rules. The goal of the work is to increase the performance of computational operations in the process of detecting the edges of objects by reducing the number of passes through the original image. The process of edge detection is carried out by some computational operations of the Canny detector with the replacement of the most complex procedures. In the proposed methods, fuzzification of eight input variables is carried out after determining the gradient and the angle of its direction. The input variables are the gradient difference between the central and adjacent cells in a frame of size 3х3. Then a base of fuzzy rules is built. In the first method, four fuzzy rules and one pass are excluded depending on the angle of gradient direction. In the second method, sixteen fuzzy rules themselves set the angle of the gradient direction, while eliminating two passes along the image. The gradient difference between the central cell and adjacent cells makes it possible to take into account the shape of the gradient distribution. Then, based on the center of gravity method, the resulting variable is defuzzified. Further use of fuzzy a-cut makes it possible to binarize the resulting image with the selection of object edges on it. The presented experimental results showed that the noise level depends on the value of the a-cut and the parameters of the labels of the trapezoidal membership functions. The software was developed to evaluate fuzzy edge detection methods. The limitation of the two methods is the use of piecewise-linear membership functions. Experimental studies of the performance of the proposed edge detection approaches have shown that the time of the first fuzzy method is 18% faster compared to the Canny detector and 2% faster than the second fuzzy method. However, during the visual assessment, it was found that the second fuzzy method better determines the edges of objects.

An algorithm of fuzzy edge detection for wetland remote sensing image based on fuzzy theory

An algorithm of fuzzy edge detection for wetland remote sensing image based on fuzzy theory

A fuzzy-enhanced deep learning approach for early detection of Covid-19 pneumonia from portable chest X-ray images

The Covid-19 pandemic is the defining global health crisis of our time. Chest X-Rays (CXR) have been an important imaging modality for assisting in the diagnosis and management of hospitalised Covid-19 patients. However, their interpretation is time intensive for radiologists. Accurate computer aided systems can facilitate early diagnosis of Covid-19 and effective triaging. In this paper, we propose a fuzzy logic based deep learning (DL) approach to differentiate between CXR images of patients with Covid-19 pneumonia and with interstitial pneumonias not related to Covid-19. The developed model here, referred to as CovNNet, is used to extract some relevant features from CXR images, combined with fuzzy images generated by a fuzzy edge detection algorithm. Experimental results show that using a combination of CXR and fuzzy features, within a deep learning approach by developing a deep network inputed to a Multilayer Perceptron (MLP), results in a higher classification performance (accuracy rate up to 81%), compared to benchmark deep learning approaches. The approach has been validated through additional datasets which are continously generated due to the spread of the virus and would help triage patients in acute settings. A permutation analysis is carried out, and a simple occlusion methodology for explaining decisions is also proposed. The proposed pipeline can be easily embedded into present clinical decision support systems.

Enhanced Edge Detection Technique in Digital Images Using Optimised Fuzzy Operation

In image processing, edge detection is a critical issue. Edge detection is a key approach for evaluating the edge of various objects in a digital image. These edges are found using the gradients, which are present in the image. The intensity and value of pixels determine the gradients. In digital images, edge detection lowers the quantity of data and filters out irrelevant data while maintaining the image's key structural features. In this paper, a new edge detection approach based on a fuzzy rule-based system is proposed. In digital image processing, the proposed method typically depends on fuzzy logic systems. The main goal of this system is to show how fuzzy logic may be used in image processing. This paper provides a fuzzy logic-based edge detection technique that uses a sharpening Gabor filter to regulate edge quality and a Gaussian filter to reduce noise caused by sharpening. This is determined by utilizing applications such as “Peak Signal to Noise Ratio (PSNR) F-Measure, and Hausdorff distance (HoD) to prove that fuzzy logic outperforms the proposed system. The findings for edge detection approaches are included in high quality. The proposed approach outperforms most commonly used traditional edge detection methods. The proposed method also reduces the number of noisy features and may be used for a wide range of images.

A fuzzy edge detector driven telegraph total variation model for image despeckling

<p style='text-indent:20px;'>Speckle noise suppression is a challenging and crucial pre-processing stage for higher-level image analysis. In this work, a new attempt has been made using telegraph total variation equation and fuzzy set theory for image despeckling. The intuitionistic fuzzy divergence function has been used to distinguish between edges and noise. To the best of the authors' knowledge, most of the studies on the multiplicative speckle noise removal process focus only on diffusion-based filters, and little attention has been paid to the study of fuzzy set theory. The proposed approach enjoys the benefits of both telegraph total variation equation and fuzzy edge detector, which is robust to noise and preserves image structural details. Moreover, we establish the existence and uniqueness of weak solutions of a regularized version of the present system using the Schauder fixed point theorem. With the proposed technique, despeckling is carried out on natural, real synthetic aperture radar, and real ultrasound images. The experimental results computed by the suggested method are reported, which are found better in terms of noise elimination and detail/edge preservation, concerning the existing approaches.</p>

De-Speckling of SAR Images with Fuzzy Filters along with Altered Preserved Edge Values

In this research, the denoising of speckled SAR image has been done with fuzzy filters (ATMED, TMED, ATMAV & TMAV). SAR image or Synthetic Aperture Radar image consists of the informatics of ISW (Internal solitary waves). A new technique has been proposed which preserved the edge pixels by fuzzy edge detection method and then altered with the filtered image-pixels by fuzzy filtration for getting the denoised image. The comparative result shows that the proposed filter performs better than the other filtered results in terms of PSNR (41.61 dB), MAE (1.47), MSE (4.54) for TMAVxAPE & SSIM (81%) for ATMEDwAPE. The proposed method in this research shows better SSI (Spackle Suppression Index) value. Therefore the experimental result illustrates that the suggested fuzzy filter is much more capable of simultaneously protecting edges and suppressing speckle noise. This research will be beneficial to remove spackle noise from SAR images and can be used for remote sensing and mapping of surface area of earth.