School of Computer & Information Engineering, Jiangxi Normal University, Nanchang 330022, China.
School of Information Engineering, Nanchang Institute of Technology, Nanchang 330099, China.
Comput Math Methods Med. 2021 May 17;2021:5868949. doi: 10.1155/2021/5868949. eCollection 2021.
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.
随着科学技术的飞速发展,超声越来越受到人们的关注,在工程、诊断和检测中得到了广泛的应用。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,并将其应用于医学超声肝脏图像识别。首先,对超声肝脏图像进行灰度化,并选择图像的感兴趣区域。其次,基于空间灰度共生矩阵独立矩阵、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、肝硬化和肝癌超声图像进行分类识别。最后,针对免疫算法的不足,结合支持向量机形成优化的免疫算法,提高了超声肝脏图像分类识别的性能。仿真表明,本文能有效对正常肝、肝硬化和肝癌超声图像进行分类。与传统的免疫算法相比,本文将免疫算法与支持向量机相结合,优化的免疫算法能有效提高超声肝脏图像分类识别的性能。