Image Segmentation Method With Improved GA Optimization of Two-Dimensional Maximum Entropy
Image segmentation is the cornerstone that determines the effectiveness of image processing, but traditional image segmentation methods have issues such as long computation time, low recognition accuracy, and poor anti-interference ability. To address this issue, research improves the genetic algori...
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IEEE
2024-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10771768/ |
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| author | Jin Wang Yanli Tan Xiaoning Bo Guoqin Li |
| author_facet | Jin Wang Yanli Tan Xiaoning Bo Guoqin Li |
| author_sort | Jin Wang |
| collection | DOAJ |
| description | Image segmentation is the cornerstone that determines the effectiveness of image processing, but traditional image segmentation methods have issues such as long computation time, low recognition accuracy, and poor anti-interference ability. To address this issue, research improves the genetic algorithm using adaptive hybridization and adaptive mutation probability, and combines it with the Bat algorithm to optimize the local optimization problem of the image. The sparrow algorithm is utilized to optimize the two-dimensional maximum entropy of the image, and the nonlinear inertia weight factor is brought to optimize the local search ability. The Levy flight constant is used to overcome the local optimization problem. The experiment findings indicate that the optimized algorithm improves the similarity of medical image features by an average of 11.2%, reduces segmentation accuracy by 2.6% under noise interference compared to other algorithms, and has an average peak signal-to-noise ratio 0.96 higher than other algorithms. From this, the improved algorithm greatly raises the similarity of segmented image features, has stronger resistance to noise interference than other algorithms, and significantly improves the recognition accuracy of different parts of the image. The improved algorithm provides a reference for subsequent image processing research. |
| format | Article |
| id | doaj-art-7bb575793a8c4f32a9ae8cd6d5e6d4e2 |
| institution | OA Journals |
| issn | 2169-3536 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-7bb575793a8c4f32a9ae8cd6d5e6d4e22025-08-20T02:33:48ZengIEEEIEEE Access2169-35362024-01-011218327918329210.1109/ACCESS.2024.350879610771768Image Segmentation Method With Improved GA Optimization of Two-Dimensional Maximum EntropyJin Wang0https://orcid.org/0009-0004-2348-632XYanli Tan1Xiaoning Bo2Guoqin Li3Department of Electronic Engineering, Taiyuan Institute of Technology, Taiyuan, ChinaCollege of Computing, Informatics and Mathematics, Universiti Teknologi MARA (UiTM), Selangor, MalaysiaDepartment of Electronic Engineering, Taiyuan Institute of Technology, Taiyuan, ChinaCollege of Computing, Informatics and Mathematics, Universiti Teknologi MARA (UiTM), Selangor, MalaysiaImage segmentation is the cornerstone that determines the effectiveness of image processing, but traditional image segmentation methods have issues such as long computation time, low recognition accuracy, and poor anti-interference ability. To address this issue, research improves the genetic algorithm using adaptive hybridization and adaptive mutation probability, and combines it with the Bat algorithm to optimize the local optimization problem of the image. The sparrow algorithm is utilized to optimize the two-dimensional maximum entropy of the image, and the nonlinear inertia weight factor is brought to optimize the local search ability. The Levy flight constant is used to overcome the local optimization problem. The experiment findings indicate that the optimized algorithm improves the similarity of medical image features by an average of 11.2%, reduces segmentation accuracy by 2.6% under noise interference compared to other algorithms, and has an average peak signal-to-noise ratio 0.96 higher than other algorithms. From this, the improved algorithm greatly raises the similarity of segmented image features, has stronger resistance to noise interference than other algorithms, and significantly improves the recognition accuracy of different parts of the image. The improved algorithm provides a reference for subsequent image processing research.https://ieeexplore.ieee.org/document/10771768/Genetic algorithmadaptive mutation probabilitysparrow algorithmimage segmentationtwo-dimensional maximum entropy |
| spellingShingle | Jin Wang Yanli Tan Xiaoning Bo Guoqin Li Image Segmentation Method With Improved GA Optimization of Two-Dimensional Maximum Entropy IEEE Access Genetic algorithm adaptive mutation probability sparrow algorithm image segmentation two-dimensional maximum entropy |
| title | Image Segmentation Method With Improved GA Optimization of Two-Dimensional Maximum Entropy |
| title_full | Image Segmentation Method With Improved GA Optimization of Two-Dimensional Maximum Entropy |
| title_fullStr | Image Segmentation Method With Improved GA Optimization of Two-Dimensional Maximum Entropy |
| title_full_unstemmed | Image Segmentation Method With Improved GA Optimization of Two-Dimensional Maximum Entropy |
| title_short | Image Segmentation Method With Improved GA Optimization of Two-Dimensional Maximum Entropy |
| title_sort | image segmentation method with improved ga optimization of two dimensional maximum entropy |
| topic | Genetic algorithm adaptive mutation probability sparrow algorithm image segmentation two-dimensional maximum entropy |
| url | https://ieeexplore.ieee.org/document/10771768/ |
| work_keys_str_mv | AT jinwang imagesegmentationmethodwithimprovedgaoptimizationoftwodimensionalmaximumentropy AT yanlitan imagesegmentationmethodwithimprovedgaoptimizationoftwodimensionalmaximumentropy AT xiaoningbo imagesegmentationmethodwithimprovedgaoptimizationoftwodimensionalmaximumentropy AT guoqinli imagesegmentationmethodwithimprovedgaoptimizationoftwodimensionalmaximumentropy |