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This study presents, for the first time, an ultrasound method combining focused and single plane wave transmissions to achieve single calibration, non-invasive, cuffless blood pressure monitoring. High-quality echo signals obtained from focused transmissions were first used to identify the optimal segment of the radial artery wall. Then, single plane wave signals were utilized to track this segment frame-by-frame and calculate local pulse wave velocity (PWV). Finally, the M-K equation was applied to estimate systolic and diastolic blood pressures. To validate the effectiveness of the proposed algorithm, 25 volunteers participated in the study. Data were collected using both an oscillometric cuff-based blood pressure monitor and our ultrasound system in four states: morning, evening, pre-exercise, and post-exercise. A total of 300 sets of comparative data were obtained. The overall mean deviation for systolic blood pressure was $2.2~\pm ~2.1$ mmHg, and for diastolic blood pressure, it was $2.1~\pm ~2.2$ mmHg. To our knowledge, this is the first study to achieve real-time blood pressure calculation within seconds using a single-array ultrasound transducer. Compared to existing non-invasive continuous blood pressure monitoring methods, this approach is faster and provides more accurate vascular wall information. Significance: Combined with affordable hardware, this method holds significant potential for the development of wearable devices for real-time cardiovascular health monitoring.

This research introduces a new method using deep learning to automatically identify animal footprints (spoors) in images. This can help reduce conflicts between humans and wildlife. The method combines two techniques decoder convolutional neural networks (de-CNNs) and Transformers: de-CNNs, which excel at extracting image features, and Transformers, which are great at recognizing shapes and edges. Our method leverages the strengths Canny edge detection (CannyED) in de-CNN-Transformers combination. This improves both accuracy and understanding of the model's decisions. Detected edges are segmented and reinforced for robustness. To ensure accurate classification, the system's learned active contour prioritizes key footprint areas by meticulously examining four critical regions: tarsal pad, spoor width, and hind pad to classify carnivore species. This targeted approach offers advantages: Sharper Vision: CannyED with Transformers improves accuracy in identifying spoors. Clearer Understanding: Focusing on specific features helps us understand how the model makes predictions. Targeted Analysis: Analysing crucial footprint regions increases efficiency and reduces reliance on irrelevant information. Experiments showed this method (de-CNN-Transformer) outperforms existing ones, achieving 95% accuracy in edge detection and 88% in classifying carnivore spoors from 2D aerial imagery. This research presents a promising tool for mitigating human-wildlife conflict through automatic footprint recognition. [ABSTRACT FROM AUTHOR]

Contour extraction is a core task in computer vision, serving as the foundation for object detection, segmentation, and scene understanding across various applications, including autonomous vehicles, medical imaging, and industrial automation. This paper provides an in-depth review of both classical and modern artificial intelligence (AI)-based contour extraction models. The classical methods, such as edge detectors and gradient-based operators, alongside advanced AI models, including convolutional neural networks and semantic segmentation architectures are reviewed in it. By examining each model’s strengths, limitations, and applicability to diverse real-world tasks, we aim to provide a comprehensive guide to contour extraction, highlight key challenges, and propose potential research directions in this evolving field.

This paper surveys various edge detection techniques in image processing, focusing on their applicability to disease detection. Many researchers encompass studies conducted in the context of various crops and fruits, shedding light on their effectiveness and adaptability. However, the more techniques are used and improved, less comparison has been made between them to look further at their challenges, such as noise sensitivity, scale variability, edge linking, and real-world variability. Also, the study will systematically survey and analyze literature on the ability of edge detection, including classical methods like Robert, Sobel, Prewitt, and Canny, as well as more advanced techniques such as gradient-based and Gaussian-based. This research aims to comprehensively understand the strengths and limitations of different edge detection techniques and can be used as a reference point for selecting and enhancing novel techniques in image processing. Overview, this paper makes a substantial contribution to the field by addressing both traditional edge detection in image processing and applied disease detection. It serves as a comprehensive guide, offering insights, practical advice, and a consolidated view of current research trends, and highlights the potential of edge detection in contributing to advancements in disease detection methodologies making it a valuable resource for researchers and practitioners.

The lungs are one of the vital organs in the human body. Not only play a role in the respiratory system, the lungs are also responsible for the human circulatory system. Supporting examinations can also facilitate medical workers in determining the diagnosis. Usually a lung examination is complemented by a chest X-ray examination procedure. This examination aims to see directly and assess the severity of lung conditions. With current technological advances, image analysis can be done easily. Through digital image processing methods, information can be obtained from images that can be used for analysis as a support for diagnoses in the world of health. Image segmentation is a method in which digital images are divided into several segments or subgroups based on the characteristics of the pixels in the image. In this study, clustering with the K-Means method will be carried out on the results of segmentation of x-ray images of lung diseases, namely Covid-19, Tuberculosis, and Pneumonia. The segmentation method that will be implemented is the Chan-Vese Method and the Canny Edge Detection Method. This research shows that the results of the accuracy of applying the K-Means Clustering method to Chan-Vese and Canny Edge-Based Image Segmentation are 80%.

Abstract With the continuous development of science and technology, intelligent surveillance technology using image processing and computer vision is also progressing. To improve the performance of target detection and tracking, an improved target tracking method is proposed, which uses a combination of the Canny operator and morphology for the detection part, and a Kalman filter extended Kernel Correlation Filter (KCF) tracking algorithm approach for the tracking part. First, a convolution kernel of $$3\times 3$$ 3 × 3 is improved to a convolution kernel of $$2\times 2$$ 2 × 2 in the traditional Canny algorithm, and the pixel gradient in the diagonal direction is increased. Secondly, a mathematical morphology theory of nonlinear filtering is applied to the Canny edge detection algorithm, and this method effectively improves the clarity of image edges. Finally, the extended kernel correlation filtering algorithm is applied to video surveillance and Online Object Tracking Benckmark2013 (OTB2013) datasets for testing. The experimental results show that the method proposed in this paper can accurately detect moving targets and the algorithm has good accuracy and success rate.

With the continuous development of science and technology, intelligent surveillance technology using image processing and computer vision is also progressing. To improve the performance of target detection and tracking, an improved target tracking method is proposed, which uses a combination of the Canny operator and morphology for the detection part, and a Kalman filter extended Kernel Correlation Filter (KCF) tracking algorithm approach for the tracking part. First, a convolution kernel of 3 × 3 is improved to a convolution kernel of 2 × 2 in the traditional Canny algorithm, and the pixel gradient in the diagonal direction is increased. Secondly, a mathematical morphology theory of nonlinear filtering is applied to the Canny edge detection algorithm, and this method effectively improves the clarity of image edges. Finally, the extended kernel correlation filtering algorithm is applied to video surveillance and Online Object Tracking Benckmark2013 (OTB2013) datasets for testing. The experimental results show that the method proposed in this paper can accurately detect moving targets and the algorithm has good accuracy and success rate. [ABSTRACT FROM AUTHOR]

Aiming to address the problems of uneven brightness and small defects of low contrast on the surface of lithium-ion battery electrode (LIBE) coatings, this study proposes a defect detection method that combines background reconstruction with an enhanced Canny algorithm. Firstly, we acquire and pre-process the electrode coating image, considering the characteristics of the electrode coating process and defects. Secondly, background reconstruction and the difference method are introduced to achieve the rough localization of coating defects. Furthermore, the image with potential defects undergoes enhancement through improved Gamma correction, and the PSO-OTSU algorithm with adaptive searching is applied to determine the optimal segmentation. Finally, precise defect detection is accomplished using the improved Canny algorithm and morphological processing. The experimental results show that, compared with the maximum entropy method, the region growth method, and the traditional Canny algorithm, the algorithm in this paper has a higher segmentation accuracy for defects. It better retains defect edge features and provides a more accurate detection effect for defects like scratches, dark spots, bright spots, metal leakage, and decarburization, which are difficult to recognize on the background of coating areas of electrodes. The proposed method is suitable for the online real-time defect detection of LIBE coating defects in actual lithium-ion battery industrial production. [ABSTRACT FROM AUTHOR]

Low-light images are captured in situations where the environment lighting is poor or the camera hardware is not capable of producing good quality images. These types of images tend to have low contrast, blurry details, noise, and color distortion. In computer vision applications, image brightness plays a crucial role, and therefore, low-light image enhancement is used as a preprocessing step. In this study, we have improved the Low-Light Enhancement Network with Global Awareness (GLADNet) method by adding a UNet based edge information extraction unit. The channel attention mechanism was also incorporated into the edge information extraction unit to achieve color preservation. Our experiments show that our proposed method has achieved higher PSNR, SSIM, and FSIM metrics compared to reference images. Additionally, it has produced lower NIQE and BRISQUE values for non-reference performance evaluation. Moreover, our proposed method removes noise better and produces visual results that are closer to the target images. [ABSTRACT FROM AUTHOR]

One of the critical issues of image processing, defined as obtaining useful information from the image and improving the quality of the image, is edge detection. How edge detection performance will be affected by adding morphological operators to edge detection algorithms is among the issues that have not been fully resolved. In the study, Canny and Sobel edge detection algorithms were applied to different milling cutters used in machinability. Morphological operators were applied to the determined edges, and their effects on the edges were examined. Mean Square Error (MSE) and Peak Signal Noise Ratio (PSNR) values were used to compare the performances of edge detection algorithms. According to MSE and PSNR results, it was seen that the Canny algorithm gave better results than the Sobel algorithm. In addition, it was concluded that the images obtained as a result of the applied morphological operations provided better performance than the images that were not applied for both Canny and Sobel algorithms. [ABSTRACT FROM AUTHOR]

Background: Thorax CT scan is one of the medical supports that contributes most importantly to the diagnosis, especially cases of COVID-19. The disadvantage that has a CT scan image is noise. When the noise is high then the Signal Noise Ratio (SNR) value produced is low. Canny operator edge detection technique is one solution to animate noise. Purpose: Analyzing differences in image quality and anatomical information on thorax CT scan images in COVID-19 patients before and after the application of canny operator edge detection techniques. Method: A quasi experimental research on thorax CT Scan images before and after the application of canny operator edge detection which amounted to 10 samples. Image assessment is done by measuring noise, SNR, and anatomical information. Differences in image quality (noise and SNR) are tested with paired T-tests. Anatomical information is tested with the Wilcoxon Signed Rank Test. Result: There are differences in image quality in thorax CT scan images in COVID-19 patients before and after the application of canny operator edge detection techniques, with p-value

Nutrient deficiency in Citrus reticulata (mandarin orange) plants is a critical agricultural issue that has far-reaching implications. Such deficiencies not only compromise the overall health and vigor of the mandarin orange trees but also render them more susceptible to diseases and pest infestations. Addressing this concern is of principal importance to ensure sustainable citrus cultivation and enhance crop yield. In this research, we propose a comprehensive approach to tackle the pervasive problem of nutrient deficiencies in Citrus Reticulata var. Fremont leaves. Our approach leverages cutting-edge techniques in image processing and machine learning to provide accurate and efficient detection of these deficiencies. The image data is divided into four classes: N Deficiency, P Deficiency, K Deficiency and Normal. The file sizes are compressed using a lossless compression method, resulting in an average file size reduction of 96.99%. The second stage involves applying segmentation processes to the images using the Canny and Sauvola methods. Third stage involves extracting colour and texture features from the images. The feature values will be used for the classification process in the next stage. The segmentation process employs two methods, namely Canny and Sauvola, which effectively separate the leaves from the background. The detection process is evident in the feature extraction phase, which utilizes two features: colour and texture. The fourth stage involves the classification process based on the segmentation results methods, performed separately using Artificial Neural Network (ANN) and Support Vector Machine (SVM) methods. These process results in four datasets: Canny-ANN, Canny-SVM, Sauvola-ANN, and Sauvola-SVM. The highest accuracy is achieved by the Sauvola-ANN method, with a value of 93.75%. [ABSTRACT FROM AUTHOR]

In light of growing metropolitan traffic, safety when driving has grown more and more persuasive. The majority of fatalities on the roads are caused by drivers leaving their lanes while following the required laws. Many of them are a consequence of the driver's disrupted and drowsy attitude. Lane discipline is essential for both driving and protecting pedestrians on the road. The proposed research seeks to detect lane markings in order to produce a safe and advanced traffic system. The proposed device's capabilities include displaying lane line locations to the driver on any exterior display to more complicated programs such as recognizing lane changes within the near future so that injuries caused on the roadways may be avoided. The proposed strategy aims to detect markings for lanes on the roadway by providing a video of the road using computer vision technology to reduce the incidence of accidents. The proposed system was installed in automobiles and cabs to reduce the number of accidents caused by careless driving along the road. In addition, the efficiency of motorists in transportation-related sectors can be tracked in order to detect and report driver irresponsibility and lack of focus on the roads. [ABSTRACT FROM AUTHOR]

Copyright of Computer Measurement & Control is the property of Magazine Agency of Computer Measurement & Control and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Interactive image segmentation offers useful guidance to users and can be applied in practical settings for production and daily life purposes. Nonetheless, the technology’s intrinsic limitations, including complicated interaction methods and high error rates, have impeded its further advancement with the development of computer vision. To address the issue, this study introduces a method to determine the extreme point of center point prediction. The target center serves as the “symmetric center” of the extreme point, which facilitates the search for other extreme points. Furthermore, the Canny algorithm is combined to achieve edge image detection. Moreover, the residual network is enhanced through embedding a pre-activation step, introducing a BatchNorm layer, and adding a pyramid scene parsing network. Finally, the performance of this method is verified by analyzing its Intersection over Union, segmentation accuracy, efficiency, F1 value, and other indicators. The results show that on the Pascalvoc2012 dataset, the segmentation accuracy obtained through the extreme point method can reach over 90%. The addition of the pyramid scene analysis network stabilizes its accuracy on urban landscape datasets between 92% and 96%. When the proposed image segmentation method is applied to the Grabcut dataset, its union intersection can reach 88.7%. On the self-generated complex daily scenery dataset, this method achieves segmentation accuracy of 95% with superior stability and precision. This provides a fresh methodological reference for optimizing interactive image segmentation technology further.

Calculating area and perimeter in real-world conditions has its challenges. The actual conditions include applications in the medical field to measure the presence of tumors or the condition of human organs and applications in geography to measure specific areas on a map; applications in architecture often calculate the area and perimeter of buildings, interior design, exterior design, and other uses. Technology can make it easier with automatic calculations. Mathematical methods and computer vision techniques are required to create automated systems. The Canny method is usually used, which is good enough for detecting edges but not sufficient for measuring irregular geometric shapes. This paper aims to calculate the area and perimeter of a geometric shape using the Canny method and geometry. Data samples in various forms are used in this study. Calculating area and perimeter using the Canny method involves obtaining the length (X,Y) of the RGB image converted to HSV. Edge detection values are used to calculate the area and perimeter of objects. The morphological method uses binary image input as input data. Then proceed to the convolution process with structuring and calculating the area and circumference of objects. Based on the research results, calculating the area and circumference of objects is more effective using morphological methods. However, the level of accuracy is affected by the selection of structuring elements (strels) which must be optimal and global.

On average, one in every eight women is diagnosed with breast cancer during their lifetime, and accounts for 14% of cancers in women. Since early diagnosis could improve treatment outcomes and longer survival times for patients, it is absolutely necessary to develop techniques to classify lesions within breast cancer mammograms and ultrasound images. The main goal is to determine the class of tumor present within the image, which is pivotal in diagnosing breast cancer patients. In this paper, we propose an Sobel-Canny-Gabor (SCG) model, which is a hybrid model that implements three different edge detection filters; Sobel filter, Gabor filter, and Canny filter. This model is used to enhance the appearance of the mammogram and ultrasound images, which is then fed into a classification model. Through classification, there could be a potential improvement in the results of the overall classification. Post-classification, the model is then evaluated using the metric Peak Signal-to-Noise Ratio (PSNR), which measures the quality between the original image and the compressed image. [ABSTRACT FROM AUTHOR]

Aiming at the shortcomings of the current Canny edge detection method in terms of noise removal, threshold setting, and edge recognition, this paper proposes a method for improving Canny edge detection by geo‐information mapping. The shortcomings of the traditional Canny edge detection method are analyzed by using the Canny optimal criterion and Tobler's First Law, which points out the direction of edge detection optimization by using the difference between edge properties and noise properties. The property characteristics and spatial distribution rules of edge points and edge lines are inspected using the geographic information mapping theory and technical methods, and edge identification criteria are defined at two levels of edge points and edge lines. Finally, the method model of improved Canny edge detection is constructed by combining guided filtering. The experimental results show that the improved edge detection method has the advantages of enriched edge details, accurate edge recognition, and strong self‐adaptive capability. This is a new attempt of geo‐information mapping theory and technical method in image edge detection, which has certain theoretical significance and strong practical guidance. [ABSTRACT FROM AUTHOR]

Digital image-correlation (DIC) algorithms rely heavily on the accuracy of the initial values provided by whole-pixel search algorithms for structural displacement monitoring. When the measured displacement is too large or exceeds the search domain, the calculation time and memory consumption of the DIC algorithm will increase greatly, and even fail to obtain the correct result. The paper introduced two edge-detection algorithms, Canny and Zernike moments in digital image-processing (DIP) technology, to perform geometric fitting and sub-pixel positioning on the specific pattern target pasted on the measurement position, and to obtain the structural displacement according to the change of the target position before and after deformation. This paper compared the difference between edge detection and DIC in accuracy and calculation speed through numerical simulation, laboratory, and field tests. The study demonstrated that the structural displacement test based on edge detection is slightly inferior to the DIC algorithm in terms of accuracy and stability. As the search domain of the DIC algorithm becomes larger, its calculation speed decreases sharply, and is obviously slower than the Canny and Zernike moment algorithms. [ABSTRACT FROM AUTHOR]

Canny and uncanny, like their German equivalents heimlich and unheimlich, have ambiguous double meanings. The relationship between canny, which means familiar, but also hidden threat, and uncanny which means unfamiliar, but strangely expected knowledge, is explored. The author proposes that the uncanny is derived from the reawakening of oedipal phantasies about parental coupling and the birth of siblings. He suggests that psychic growth requires toleration of the uncanny and free movement between it and canny states, corresponding with the oscillation between states, described by Bion, of Ps and D, where this promotes 'Being', through the realization of O. Childhood traumatic losses and oedipal anxieties can prevent this movement. This is illustrated by a woman who feared that the emergence of the uncanny would reveal her to be monstrous, through embodying her phantasy of being the ghost of her mother's dead sister and the damaged child of her parents' disastrous marriage. She was thus unable to allow this free movement and was condemned to 'life on the margin'. This is contrasted with extracts from Wordsworth's Prelude where 'spots of time', corresponding with screen memories, show how the liberation of the uncanny, and its free interchange with the canny, promotes mourning and growth. [ABSTRACT FROM AUTHOR]

Photographing images is used as a common detection tool during the process of bridge maintenance. The edges in an image can provide a lot of valuable information, but the detection and extraction of edge details are often affected by the image noise. This study proposes an algorithm for wavelet transform to denoise the image before edge detection, which can improve the signal-to-noise ratio of the image and retain as much edge information as possible. In this study, four wavelet functions and four decomposition levels are used to decompose the image, filter the coefficients and reconstruct the image. The PSNR and MSE of the denoised images were compared, and the results showed that the sym5 wavelet function with three-level decomposition has the best overall denoising performance, in which the PSNR and MSE of the denoised images were 23.48 dB and 299.49, respectively. In this study, the canny algorithm was used to detect the edges of the images, and the detection results visually demonstrate the difference between before and after denoising. In order to further evaluate the denoising performance, this study also performed edge detection on images processed by both wavelet transform and the current widely used Gaussian filter, and it calculated the Pratt quality factor of the edge detection results, which were 0.53 and 0.47, respectively. This indicates that the use of wavelet transform to remove noise is more beneficial to the improvement of the subsequent edge detection results. [ABSTRACT FROM AUTHOR]

Edge detection has been used widely in image processing, image recognition, and computer vision. Several edge detection techniques have been suggested over time; however, each has a lot of disadvantages. Our method has some steps. At first, the image should be converted into a grayscale image. A 3*3pattern grid is used for producing a discrete vector for any pixel in the present study. Secondly, the length of the pixel vector is increased to linearize and expand. Thirdly, the Matching Pursuit algorithm is applied to the image, and the residual part of the Matching Pursuit algorithm is extracted. Finally, two Hysteresis thresholds have been used for detecting edge pixels. The edge detection problem has been converted into a signal processing problem, so it will be less time-consuming. Moreover, the proposed algorithm is insensitive to noise, and this method can detect weak edge pixels. Also, the performance of the proposed algorithm is compared with three known edge detection algorithms, SOBEL, Laplacian, Canny, and CASENet and the results show that we have a high value of performance parameters such as PR, PSNR, and FOM compared to other algorithms. Furthermore, it is very an effective and reliable method to diagnose and treat diseases in medical images.

Copyright of Journal of Education & Science is the property of Republic of Iraq Ministry of Higher Education & Scientific Research (MOHESR) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

An expert performs bone fracture diagnosis using an X-ray image manually, which is a time-consuming process. The development of machine learning (ML), as well as deep learning (DL), has set a new path in medical image diagnosis. In this study, we proposed a novel multi-scale feature fusion of a convolution neural network (CNN) and an improved canny edge algorithm that segregate fracture and healthy bone image. The hybrid scale fracture network (SFNet) is a novel two-scale sequential DL model. This model is highly efficient for bone fracture diagnosis and takes less computation time compared to other state-of-the-art deep CNN models. The innovation behind this research is that it works with an improved canny edge algorithm to obtain edges in the images that localize the fracture region. After that, grey images and their corresponding canny edge images are fed to the proposed hybrid SFNet for training and evaluation. Furthermore, the performance is also compared with the state-of-the-art deep CNN models on a bone image dataset. Our results showed that SFNet with canny (SFNet + canny) achieved the highest accuracy, F1-score and recall of 99.12%, 99% and 100%, respectively, for bone fracture diagnosis. It showed that using a canny edge algorithm improves the performance of CNN. [ABSTRACT FROM AUTHOR]

The enlarged veins in the pampiniform venous plexus, known as varicocele disease, are typically identified using ultrasound scans. The medical diagnosis of varicocele is based on examinations made in three positions taken to the right and left testicles of the male patient. The proposed system is designed to determine whether a patient is affected. Varicocele is more frequent on the left side of the scrotum than on the right and physicians commonly depend on the supine position more than other positions. Therefore, the experimental results of this study focused on images taken in the supine position of the left testicles of patients. There are two possible vein structures in each image: a cross-section (circular) and a tube (non-circular) structure. This proposed system identifies dilated (varicocele) veins of these structures in ultrasound images in three stages: preprocessing, processing, and detection and measurement. These three stages are applied in three different color modes: Grayscale, Red-Green-Blue (RGB), and Hue, Saturation, and Value (HSV). In the preprocessing stage, the region of interest enclosing the pampiniform plexus area is extracted using a median filter and threshold segmentation. Then, the processing stage employs different filters to perform image denoising. Finally, edge detection is applied in multiple steps and the detected veins are measured to determine if dilated veins exist. Overall implementation results showed the proposed system is faster and more effective than the previous work. [ABSTRACT FROM AUTHOR]

The Global, Self-consistent, Hierarchical, High-resolution Geography (GSHHG) Database, provided by the National Centers for Environmental Information and widely used worldwide, includes global multi-scale coastline data. Although many scientists have utilized this database in their studies, the database itself has not been thoroughly evaluated. The different scales available through the GSHHG database include full resolution, high resolution, intermediate resolution, low resolution, and crude resolution. The Pearl River estuary was selected as our study site. These studies involve the use of the GSHHG database, Landsat MSS from 1978, and Landsat OLI from 2018 to evaluate the differences in coastline length and fractal dimensions among GSHHG datasets collected at five different resolution levels. This study also selected eastern and western sub-regions to compare differences between coastline maps from the GSHHG and Landsat datasets. In addition, the coastline extracted from resampled Landsat OLI data from 2018 (80 m), covering the eastern part of the Pearl River Estuary was compared with the same coastline data extracted from Landsat OLI at 30 m spatial resolution. Coastline lengths according to full resolution, high resolution, intermediate resolution, low resolution, and crude resolution data are 1509.47 km, 1398.12 km, 1212.54 km, 692.00 km, and 326.40 km, respectively. The corresponding fractal dimensions from the GSHHG database were 1.2983, 1.2832, 1.2588, 1.0990, and 1.0262 for full resolution, high resolution, intermediate resolution, low resolution, and crude resolution, respectively. Separate eastern and western segments of the Pearl River Estuary demonstrate that for a given area, full resolution of GSHHG data provides the longest and most detailed rich coastline data relative to the coastline data extracted from the Landsat data and GSHHG database. The length of the coastline extracted from the 1978 Landsat MSS was most similar to the length obtained from high-resolution GSHHG. The data on the morphology of the coastline in the 1978 Landsat MSS were much closer to the full resolution, high resolution, and intermediate resolution data obtained from GSHHG. Compared to the coastline data extracted from 80 m Landsat OLI and 30 m Landsat OLI, a 17.68% length difference was found. Fractal dimension differences were 1.7%. The results show that: (1) The length and fractal dimensions of coastline data produced at five different resolutions are substantially different. Higher-resolution data produce longer coastline lengths and more complex coastline morphologies; (2) Using coastline data extracted from Landsat data in 1978 and 2018 as references, GSHHG coastline dataset was found to be generally consistent with real data shoreline length and morphological characteristics. The morphological characteristics of the GSHHG coastline are closer to the real data from 1978, which does not reflect the current situation of the Pearl River Estuary. The GSHHG database only includes one phase of data, implying that scientists cannot accomplish studies related to coastline change without using additional data. Nevertheless, studies on comparisons between different regions of coastline could select data based on the characteristics of a study area. It is suggested that scientists select coastline data based on coastline length, complexity, and morphology when studying coastline changes in response to situations of interest.

Image analysis is an important technique that can help specialists localize, detect, and segment objects in different types of medical images such as MRI, CTs, and ultrasounds (US). In this research, we use US images to identify and segment the enlarged veins in the pampiniform venous plexus, which is called varicocele. The proposed method aims to determine whether a potential patient is affected or not. This method was evaluated using 90 US images that were taken of the left testicles of 90 patients in the Supine position. This system analyzes US images in three stages which are; preprocessing, processing, and edge detection. The Region Of Interest (ROI) of the pampiniform plexus area was extracted using Otsu segmentation with different parameters (0.1, 0.2, 0.17) and different color modes (Grayscale, YCbCr, RGB). In the processing stage, different denoising filters were used. Eventually, in the edge detection stage, four edge detectors were applied which are Canny, Soble, Prewitt, and Roberts. Results showed that the best accuracy in detecting varicocele was 78% when the YCbCr color mode yellow (y) channel was used with 0.1 Otsu segmentation and the Canny edge detector. The system also showed a Sensitivity of 91%, as the test was able to detect 91% of the people with Varicocele, and the Specificity value was 39%.