# Contrast-Invariant Edge Detection: A Methodological Advance in Medical Image Analysis
## 1 Introduction  
Edge detection plays a vital role in image processing. Its aim is to identify the boundaries or edges in images, which is indispensable for object recognition, image segmentation, and feature extraction [1]. Edge detection methods have been widely applied in multiple fields including computer vision, industrial inspection, and medical imaging. Although they share the common goal of identifying boundaries, the characteristics of the images in different fields pose unique challenges. In the field of image edge detection, there are many significant differences between medical image edge detection and natural image edge detection [2]. In terms of detection goals, medical image edge detection is mainly used for precisely locating lesion areas, and segmenting different tissue types, its core objective is to provide support for assisting in diagnosis and treatment planning [3]. In contrast, natural image edge detection is mainly applied to object recognition, contour extraction, and scene analysis. It aims to help computers understand the main objects and structures in a scene so as to supportvarious computer vision applications. From the perspective of detection requirements, when medical images are acquired by medical equipment, noise and artifacts are likely to be generated. This requires that medical image edge detection adopt methods that can effectively suppress noise and artifacts while not losing important edge information. Moreover, the grayscale distribution of medical images is narrow and the contrast is low. The gray values of different tissues and organs may overlap, and the grayscale changes in diseased tissues may be very subtle. Therefore, the edge detection algorithms for medical images need to be sensitive to subtle grayscale changes in order to accurately detect the edges of diseased tissues. Natural images contain rich colors and textures, and the shapes of objects and backgrounds are diverse, so their edge detection needs to be able to distinguish the boundaries between different objects as well as those between objects and backgrounds. Regarding the demand for accuracy, medical image edge detection requires a high level of accuracy in its results. Incorrect edge detection may lead to misdiagnosis, and the detected edges must be as close as possible to the actual boundaries of tissue structures. However, the results of natural image edge detection are allowed to have some errors to a certain extent [4]. In non-critical applications such as simple image recognition or contour-based image retrieval, even if there are small deviations in the edge detection results, as long as they can roughly reflect the shapes of objects, the requirements can still be met. In terms of real-time requirements, medical image edge detection is usually carried out in an offline state, and there is not a high requirement for real-time performance. However, natural image edge detection needs to be processed in real time in applications such as autonomous driving, which imposes relatively high requirements on the computational efficiency and response speed of algorithms. Given the imaging characteristics of medical images, such as low contrast and narrow gray scale regions, as well as their high requirement for the accuracy of edge detection, we focus our research on the field of medical image edge detection. The aim is to accurately extract the edge information with important diagnostic value so as to provide doctors with clearer and more accurate diagnostic assistance in imaging. In the process of rapid development of modern medicine, medical image-assisted diagnosis has become an indispensable and important means. Medical images generally include images obtained through various imaging technologies, and common medical images include X-ray, computed tomography (CT), magnetic resonance imaging (MRI), ultrasound imaging, and nuclear medicine imaging [5; 6; 7].