Ultrasonic Image Analysis of Cardiac Space-occupying Diseases Based on Grey Clustering Algorithm

In the computer-aided diagnosis system of ultrasound imaging, it is very important to segment the focus area in the ultrasound image accurately and quickly. If the noisy image is directly segmented in medical diagnosis, it will not meet the requirements of clinical application because the image information is not accurate enough. Therefore, some measures need to be taken, such as attenuating all kinds of noise and highlighting the contour of the target, to improve the image. In this article, an ultrasound image segmentation algorithm based on gray clustering is proposed. The medical ultrasound image is transformed into a gray feature space, and then the number of clusters is set according to the number of histogram feature peaks of the medical ultrasound image, and the medical ultrasound image is segmented in the gray feature space. Different algorithms are used to verify the ultrasonic images and compare the processing effects. The comparison and analysis of experimental results show that the ultrasonic image processing algorithm of dirty occupying diseases combined with grey clustering has the ability to search the global optimal solution and can get good ultrasonic image segmentation results.