Feature Extraction Processing Method of Medical Image Fusion Based on Neural Network Algorithm
Medical image technology is becoming more and more important in the medical field. It not only provides important information about internal organs of the body for clinical analysis and medical treatment but also assists doctors in diagnosing and treating various diseases. However, in the process of...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2021-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2021/7523513 |
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| _version_ | 1832561467588608000 |
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| author | Tianming Song Xiaoyang Yu Shuang Yu Zhe Ren Yawei Qu |
| author_facet | Tianming Song Xiaoyang Yu Shuang Yu Zhe Ren Yawei Qu |
| author_sort | Tianming Song |
| collection | DOAJ |
| description | Medical image technology is becoming more and more important in the medical field. It not only provides important information about internal organs of the body for clinical analysis and medical treatment but also assists doctors in diagnosing and treating various diseases. However, in the process of medical image feature extraction, there are some problems, such as inconspicuous feature extraction and low feature preparation rate. Combined with the learning idea of convolution neural network, the image multifeature vectors are quantized in a deeper level, which makes the image features further abstract and not only makes up for the one-sidedness of single feature description but also improves the robustness of feature descriptors. This paper presents a medical image processing method based on multifeature fusion, which has high feature extraction effect on medical images of chest, lung, brain and liver, and can better express the feature relationship of medical images. Experimental results show that the accuracy of the proposed method is more than 5% higher than that of other methods, which shows that the performance of the proposed method is better. |
| format | Article |
| id | doaj-art-f3883d27b41647328e13d36c30da1f63 |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-f3883d27b41647328e13d36c30da1f632025-02-03T01:25:01ZengWileyComplexity1076-27871099-05262021-01-01202110.1155/2021/75235137523513Feature Extraction Processing Method of Medical Image Fusion Based on Neural Network AlgorithmTianming Song0Xiaoyang Yu1Shuang Yu2Zhe Ren3Yawei Qu4The Higher Educational Key Laboratory for Measuring & Control Technology and Instrumentations of Heilongjiang Province, Harbin University of Science and Technology, Harbin 150080, ChinaThe Higher Educational Key Laboratory for Measuring & Control Technology and Instrumentations of Heilongjiang Province, Harbin University of Science and Technology, Harbin 150080, ChinaThe Higher Educational Key Laboratory for Measuring & Control Technology and Instrumentations of Heilongjiang Province, Harbin University of Science and Technology, Harbin 150080, ChinaThe Higher Educational Key Laboratory for Measuring & Control Technology and Instrumentations of Heilongjiang Province, Harbin University of Science and Technology, Harbin 150080, ChinaDepartment of Control Science and Engineering, Harbin Institute of Technology, Harbin 150000, ChinaMedical image technology is becoming more and more important in the medical field. It not only provides important information about internal organs of the body for clinical analysis and medical treatment but also assists doctors in diagnosing and treating various diseases. However, in the process of medical image feature extraction, there are some problems, such as inconspicuous feature extraction and low feature preparation rate. Combined with the learning idea of convolution neural network, the image multifeature vectors are quantized in a deeper level, which makes the image features further abstract and not only makes up for the one-sidedness of single feature description but also improves the robustness of feature descriptors. This paper presents a medical image processing method based on multifeature fusion, which has high feature extraction effect on medical images of chest, lung, brain and liver, and can better express the feature relationship of medical images. Experimental results show that the accuracy of the proposed method is more than 5% higher than that of other methods, which shows that the performance of the proposed method is better.http://dx.doi.org/10.1155/2021/7523513 |
| spellingShingle | Tianming Song Xiaoyang Yu Shuang Yu Zhe Ren Yawei Qu Feature Extraction Processing Method of Medical Image Fusion Based on Neural Network Algorithm Complexity |
| title | Feature Extraction Processing Method of Medical Image Fusion Based on Neural Network Algorithm |
| title_full | Feature Extraction Processing Method of Medical Image Fusion Based on Neural Network Algorithm |
| title_fullStr | Feature Extraction Processing Method of Medical Image Fusion Based on Neural Network Algorithm |
| title_full_unstemmed | Feature Extraction Processing Method of Medical Image Fusion Based on Neural Network Algorithm |
| title_short | Feature Extraction Processing Method of Medical Image Fusion Based on Neural Network Algorithm |
| title_sort | feature extraction processing method of medical image fusion based on neural network algorithm |
| url | http://dx.doi.org/10.1155/2021/7523513 |
| work_keys_str_mv | AT tianmingsong featureextractionprocessingmethodofmedicalimagefusionbasedonneuralnetworkalgorithm AT xiaoyangyu featureextractionprocessingmethodofmedicalimagefusionbasedonneuralnetworkalgorithm AT shuangyu featureextractionprocessingmethodofmedicalimagefusionbasedonneuralnetworkalgorithm AT zheren featureextractionprocessingmethodofmedicalimagefusionbasedonneuralnetworkalgorithm AT yaweiqu featureextractionprocessingmethodofmedicalimagefusionbasedonneuralnetworkalgorithm |