Modern veterinary imaging provides a large amount of pathological information of sick animals through veterinary B-ultrasound images, which are widely used in veterinary clinics, especially veterinary B-ultrasound diagnostic imaging instruments, which play an irreplaceable role in veterinary clinics. However, the quality of veterinary B-ultrasound images is almost the worst among medical images, so in order to make better diagnoses for veterinarians, it is particularly important to enhance their images.

In recent years, with the gradual upgrading of the depth of research on veterinary B-ultrasound images, people have conducted in-depth research on traditional veterinary B-ultrasound image enhancement methods such as grayscale transformation, histogram equalization, and median filtering. However, with the advancement of science and technology, some traditional enhancement methods are no longer suitable for complex animal pathology veterinary B-ultrasound images. Some fixed-mode enhancement methods are gradually being replaced, and emerging methods such as fuzzy enhancement

Veterinary B-ultrasound BXL-DB20

are attracting the attention of many researchers. At present, in China, some popular enhancement methods that have been successfully applied in the field of digital image enhancement, such as histogram local equalization, edge detection combining Sobel operator and Canny operator, classic Pal and King fuzzy enhancement algorithm, veterinary B-ultrasound image enhancement based on wavelet, etc., are all highly praised; abroad, there are also well-received Curveletllg transform, etc. The application prospects of these methods in veterinary B-ultrasound image processing have yet to be explored.

Based on the characteristics of veterinary B-ultrasound images, some people have added an automatic threshold selection algorithm on the basis of the grayscale-based fuzzy enhancement method, and combined the two to make veterinary B-ultrasound image processing more convenient and humane.

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