There are many methods for segmenting animal B-ultrasound images, among which region growing method is a commonly used method, and its steps and principles are as follows.

The region growing method for animal B-ultrasound image segmentation is a serial segmentation method. The characteristic of serial segmentation method is to decompose the processing process into multiple sequential steps, where the processing of subsequent steps needs to be determined based on the results of the previous steps. Judgment is based on pre-defined criteria.

The basic idea of the region growing method for animal B-ultrasound image segmentation is to gather pixels with similar properties to form regions. In the simple form of this method, a seed is first manually given as the starting point for growth, and then pixels in the neighborhood around the seed pixel that have the same or similar properties to the seed pixel (determined according to some predetermined growth or similarity criteria) are merged into the region where the seed pixel is located. Continue the above process by treating these new pixels as new seed pixels until all eligible pixels are included, and then a region will grow.

The region growing method for animal B-ultrasound image segmentation is generally not used alone, but is included in a series of processing procedures, specifically used to depict small and simple structures such as tumors and wounds. Its main drawback is that each region that needs to be extracted must be manually given a seed point, so if there are multiple regions, the corresponding number of seeds must be given. This method is also sensitive to noise, which can result in porous or even discontinuous areas. On the contrary, local and extensive influences can also connect previously separated areas.

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