With the rapid development of science and technology, ultrasound has been paid more and more attention by people, and it is widely used in engineering, diagnosis, and detection. In this paper, an ultrasonic image recognition method based on immune algorithm is proposed for ultrasonic images, and its method is applied to medical ultrasound liver image recognition. Firstly, this paper grays out the ultrasound liver image and selects the region of interest of the image. Secondly, it extracts the feature based on spatial gray matrix independent matrix, spatial frequency decomposition, and fractal features. Then, the immune algorithm is used to classify and identify the normal liver, liver cirrhosis, and liver cancer ultrasound images. Finally, based on the deficiency of the immune algorithm, it is combined with the support vector machine to form an optimized immune algorithm, which improves the performance of ultrasonic liver image classification and recognition. The simulation shows that this paper can effectively classify the normal liver, liver cirrhosis, and liver cancer ultrasound images. Compared with the traditional immune algorithm, this paper combines the immune algorithm with the support vector machine, and the optimized immune algorithm can effectively improve the performance of ultrasonic liver image classification and recognition.

中文翻译：

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基于优化免疫算法的超声图像识别研究

随着科学技术的飞速发展，超声越来越受到人们的重视，在工程、诊断、检测等方面得到广泛应用。本文针对超声图像提出了一种基于免疫算法的超声图像识别方法，并将该方法应用于医学超声肝脏图像识别中。首先，本文将超声肝脏图像灰度化，选择图像的感兴趣区域。其次，基于空间灰度矩阵独立矩阵、空间频率分解和分形特征提取特征。然后利用免疫算法对正常肝脏、肝硬化、肝癌超声图像进行分类识别。最后，基于免疫算法的不足，结合支持向量机形成优化的免疫算法，提高超声肝脏图像分类识别性能。仿真表明，本文能够有效地对正常肝脏、肝硬化和肝癌超声图像进行分类。与传统免疫算法相比，本文将免疫算法与支持向量机相结合，优化后的免疫算法能够有效提高超声肝脏图像分类识别的性能。