Research on Fundus Image Mosaic Method Based on Genetic Algorithm
Retinal image mosaic is the key to detect common diseases, and the existing image mosaic methods are difficult to solve the problems of low contrast of fundus images and geometric distortion between images in different fields of view. To solve the problem of noise in retinal fundus images, an image...
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| Format: | Article |
| Language: | English |
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Wiley
2021-01-01
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| Series: | Advances in Multimedia |
| Online Access: | http://dx.doi.org/10.1155/2021/6060691 |
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| _version_ | 1832565651224395776 |
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| author | Shuang Wang |
| author_facet | Shuang Wang |
| author_sort | Shuang Wang |
| collection | DOAJ |
| description | Retinal image mosaic is the key to detect common diseases, and the existing image mosaic methods are difficult to solve the problems of low contrast of fundus images and geometric distortion between images in different fields of view. To solve the problem of noise in retinal fundus images, an image mosaic algorithm based on the genetic algorithm was proposed. Firstly, a series of morphological pretreatment was performed on the fundus images. Then, the vascular network is extracted by obtaining the maximum entropy of the image to determine the threshold value. The similarity of the image to be spliced is a feature, and the genetic algorithm is used to solve the optimal parameters to achieve the maximum similarity. By smoothing the image, a clear image with minimum noise is obtained. Experimental results show that the proposed algorithm can effectively realize the image mosaic of the fundus. The method proposed in this paper can provide support for high-precision automatic stitching of multiple single-mode color fundus images. |
| format | Article |
| id | doaj-art-694a604f7cf641778576b913cf734b62 |
| institution | Kabale University |
| issn | 1687-5699 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advances in Multimedia |
| spelling | doaj-art-694a604f7cf641778576b913cf734b622025-02-03T01:07:07ZengWileyAdvances in Multimedia1687-56992021-01-01202110.1155/2021/6060691Research on Fundus Image Mosaic Method Based on Genetic AlgorithmShuang Wang0Network Management CenterRetinal image mosaic is the key to detect common diseases, and the existing image mosaic methods are difficult to solve the problems of low contrast of fundus images and geometric distortion between images in different fields of view. To solve the problem of noise in retinal fundus images, an image mosaic algorithm based on the genetic algorithm was proposed. Firstly, a series of morphological pretreatment was performed on the fundus images. Then, the vascular network is extracted by obtaining the maximum entropy of the image to determine the threshold value. The similarity of the image to be spliced is a feature, and the genetic algorithm is used to solve the optimal parameters to achieve the maximum similarity. By smoothing the image, a clear image with minimum noise is obtained. Experimental results show that the proposed algorithm can effectively realize the image mosaic of the fundus. The method proposed in this paper can provide support for high-precision automatic stitching of multiple single-mode color fundus images.http://dx.doi.org/10.1155/2021/6060691 |
| spellingShingle | Shuang Wang Research on Fundus Image Mosaic Method Based on Genetic Algorithm Advances in Multimedia |
| title | Research on Fundus Image Mosaic Method Based on Genetic Algorithm |
| title_full | Research on Fundus Image Mosaic Method Based on Genetic Algorithm |
| title_fullStr | Research on Fundus Image Mosaic Method Based on Genetic Algorithm |
| title_full_unstemmed | Research on Fundus Image Mosaic Method Based on Genetic Algorithm |
| title_short | Research on Fundus Image Mosaic Method Based on Genetic Algorithm |
| title_sort | research on fundus image mosaic method based on genetic algorithm |
| url | http://dx.doi.org/10.1155/2021/6060691 |
| work_keys_str_mv | AT shuangwang researchonfundusimagemosaicmethodbasedongeneticalgorithm |