veterinary B-ultrasound animal cardiovascular image analysis plays an important role in the diagnosis of cardiovascular diseases. Due to the differences in brightness and grayscale of different lesion areas, it is very valuable to use computer pattern recognition technology and curve fitting algorithm to measure the area, percentage, brightness and grayscale statistics of the lesion area, and obtain the grayscale change curve of each corresponding area. In the cardiovascular image analysis system designed by us, the minimum distance method in pattern recognition technology is used to count the discontinuous areas. At the same time, the curve fitting method is used to make a grayscale-time curve for the grayscale change of each frame image and calculate the grayscale peak value, peak time, first half peak time, second half peak time and area under the curve. It has high application value for veterinary clinical diagnosis and scientific research.

Veterinary B-ultrasound animal cardiovascular image analysis is one of the most common and basic methods for diagnosing cardiovascular diseases. Usually, veterinarians identify the lesion area by directly observing veterinary B-ultrasound images, which inevitably brings certain errors, let alone quantitative analysis. The development and application of computer pattern recognition technology makes quantitative analysis of veterinary B-ultrasound images possible, which has been valued by veterinarians.

In the animal cardiovascular image analysis system for veterinary B-ultrasound images, button controls, page controls, scroll bar controls, text controls, label controls and database controls are mainly used. Database controls and page controls are used to control one or several images of each sick animal. If an image is large, a scroll bar control is used to control it. The main functional modules of this system (such as image sampling and classification) are implemented using button controls, and the category number of each category is also controlled by a page control.