## 2 Related Works
The results revealed that the customized Canny edge detection method exhibited a higher accuracy rate of 95% and a specificity of 86%, surpassing the performance of the Prewitt edge detection method. Their findings underscore the importance of utilizing cutting-edge technologies to enhance the efficiency and precision of bone fracture detection, ultimately contributing to more effective healthcare practices. [PERSON] et al. [14] discussed the significance of edge detection in image processing and analysis, focusing on its crucial role in human visual recognition and target identification. This paper underscored the importance of edge detection quality for successful target recognition and the practical value of signal and image edge detection research. The study that the authors conducted aimed to address the limitations of traditional edge detection algorithms; thus, they proposed an algorithm based on wavelet modulus maxima to improve edge detection. They focused on dislocation phenomena, threshold selection, and multi-scale edge fusion. To address these challenges, they tailored an enhanced self-adaptive layered threshold algorithm and a weighted multi-scale edge fusion algorithm. The influence of wavelet characteristics on image edge detection was explored, emphasizing the conditions for wavelet construction and evaluation criteria for edge detection methods. [PERSON] [15] focused on generating facial contours using a bidirectional algorithm to improve edge detection accuracy with applications to facial reconstruction and medical applications. He highlighted the importance of image segmentation and the 3D reconstruction of faces as relevant research areas in image processing, as it leads to identifying tissue edges. Also, he mentioned histogram equalization as a technique used to enhance contrast in medical computed tomography (CT) images as it contributes to enhanced visualization of anatomical details. He emphasised the relevance of edge detection in digital image processing, especially in medical and plastic surgery applications where precision in contour identification is paramount. [PERSON] et al. [16] developed a hybrid edge detection approach that combines the [PERSON] and Sobel edge detection techniques and applied it to identify discontinuities in images of the Tocklai Vegetative Tea Clone Series TV1, TV9, and TV10 in Assam. This approach provides accurate information on the boundaries of TV1, TV9, and TV10 by using a dilation morphological operation to eliminate flaws and compute boundaries. They compared their approach with popular edge detection techniques like [PERSON], [PERSON], [PERSON], [PERSON], Laplacian of Gaussian, and Gaussian based on noise level parameters including the mean square error (MSE), peak signal noise ratio (PSNR), and signal to noise ratio (SNR). Their results showed that the hybrid edge detection approach, combined with dilation using the morphological operation, is efficient and can be widely used for detecting the edges of the Tocklai Vegetative Tea Clone Series. [PERSON] et al. [17] applied the ant colony optimization (ACO) algorithm, [PERSON], and [PERSON] edge detection techniques to illustrate classical, dynamic, and intelligent edge detection schemes. This approach explored the complex skyline digital images as case studies and conducted a qualitative analysis to examine the visual appeals of detection outcomes based on red-blue-green (RGB) scales. In contrast, quantitative analysis compares edge detection outcomes based on gray and binary scales in the frequency and spatial domains. This study provided an extensive evaluation of the overall quality of edge detection schemes. These works exemplify potential applications that might benefit from edge filters like the one proposed in this paper.