Veterinary B-ultrasound examination is widely used in clinical practice because of its safety and intuitiveness. However, the current diagnosis of veterinary B-ultrasound images often relies on the experience of clinicians. The human eye is too subjective. Therefore, it is very important to apply computers to edge detection and extraction of veterinary B-ultrasound images. With the accurate edge of the ward, the area, perimeter and other morphological parameters of the ward can be measured, providing an objective basis for diagnosis. In order to improve veterinarians' ability to distinguish tiny lesions, it is particularly important to apply edge detection technology.

 Edge detection of veterinary B-ultrasound digital images is a very important basis for image analysis fields such as image segmentation, target edge recognition, and region shape extraction. It is also an important attribute for extracting features of veterinary B-ultrasound images in veterinary B-ultrasound image recognition. The edge of an object appears in the form of discontinuous local features, which refers to the part of the veterinary B-ultrasound image with the most significant changes in local brightness, such as sudden changes in gray value, color, texture, etc., while the object's Edges are also where different areas break down. The edges of veterinary B-ultrasound images have two characteristics: direction and amplitude. Usually the grayscale changes along the edge are gentle, while the grayscale changes of pixels perpendicular to the edge are violent.

 The basic idea of using edge detection to segment veterinary B-ultrasound images is to first detect the edge points in the veterinary B-ultrasound images, and then connect the edge points into outlines according to a certain method to form the segmentation area. Since the edge is the decomposition line between the target and the background to be extracted, only by extracting the edge can the target and the background be distinguished.