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基于优化免疫算法的超声图像识别研究。

Research on Ultrasonic Image Recognition Based on Optimization Immune Algorithm.

机构信息

School of Computer & Information Engineering, Jiangxi Normal University, Nanchang 330022, China.

School of Information Engineering, Nanchang Institute of Technology, Nanchang 330099, China.

出版信息

Comput Math Methods Med. 2021 May 17;2021:5868949. doi: 10.1155/2021/5868949. eCollection 2021.

DOI:10.1155/2021/5868949
PMID:34055040
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8149231/
Abstract

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.

摘要

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。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bee3/8149231/f603fe67b838/CMMM2021-5868949.008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bee3/8149231/d071ead82dd5/CMMM2021-5868949.001.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bee3/8149231/1af56e7cc2ac/CMMM2021-5868949.005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bee3/8149231/95a5dcc75e10/CMMM2021-5868949.006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bee3/8149231/f086c566f8b2/CMMM2021-5868949.007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bee3/8149231/f603fe67b838/CMMM2021-5868949.008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bee3/8149231/d071ead82dd5/CMMM2021-5868949.001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bee3/8149231/e500a6f283ad/CMMM2021-5868949.002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bee3/8149231/f187cd2d80e2/CMMM2021-5868949.003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bee3/8149231/bde8342fbcfc/CMMM2021-5868949.004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bee3/8149231/1af56e7cc2ac/CMMM2021-5868949.005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bee3/8149231/95a5dcc75e10/CMMM2021-5868949.006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bee3/8149231/f086c566f8b2/CMMM2021-5868949.007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bee3/8149231/f603fe67b838/CMMM2021-5868949.008.jpg

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本文引用的文献

1
The Artificial Intelligence-Enabled Medical Imaging: Today and Its Future.人工智能赋能的医学成像：现状与未来。
Chin Med Sci J. 2019 Jun 30;34(2):71-75. doi: 10.24920/003615.
2
Biomedical Text Categorization Based on Ensemble Pruning and Optimized Topic Modelling.基于集成剪枝和优化主题建模的生物医学文本分类
Comput Math Methods Med. 2018 Jul 22;2018:2497471. doi: 10.1155/2018/2497471. eCollection 2018.
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Psychophysiology. 2017 Jan;54(1):100-113. doi: 10.1111/psyp.12682.
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Multiobjective immune algorithm with nondominated neighbor-based selection.基于非支配邻域选择的多目标免疫算法
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