In natural images, contours refer to those parts with drastic grayscale changes, which can be approximately modeled using step matrix contours. In veterinary medicine, small spots in veterinary B-ultrasound images can provide useful information about microscopic tissues. Cellular neural networks, first as a tool to replace the globally connected Hopfield neural network, are widely used in image processing, biological vision, advanced brain function and other research. In particular, some cellular neural network templates are used for the removal of tiny objects, edge detection, convex corner detection, diagonal detection, large-scale connectivity detection, etc. in image processing.

The CD cellular neural network is used to detect the veterinary B-ultrasound images of the patient's liver. The data processed by each sick animal is fitted with a tenth-order polynomial. It is found that the coefficients of the tenth-order polynomial seem to be related to the degree of damage to the liver of the sick animal.

The cellular neural network method is used to detect the contours of the veterinary B-ultrasound images of the liver, thereby obtaining all the contour information in the veterinary B-ultrasound images of the animal liver, and then the veterinary B-ultrasound images are analyzed by mathematical methods to find the data corresponding to each veterinary B-ultrasound image, which can determine the relationship between graphics and data, so as to describe the liver lesions through data. Provide a quantitative analysis method for veterinary clinical diagnosis, thereby assisting veterinarians in diagnosing animal diseases.

Some studies suggest that the equilibrium point information of the CD cellular neural network state equation is meaningful for analyzing animal liver veterinary B-ultrasound images. Many unknown engineering and mathematical fields related to cellular neural networks need to be further explored.