A Straightforward Bifurcation Pattern-Based Fundus Image Registration Method
Fundus image registration is crucial in eye disease examination, as it enables the alignment of overlapping fundus images, facilitating a comprehensive assessment of conditions like diabetic retinopathy, where a single image’s limited field of view might be insufficient. By combining multiple images...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-09-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/23/18/7809 |
| _version_ | 1797577019470381056 |
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| author | Jesús Eduardo Ochoa-Astorga Linni Wang Weiwei Du Yahui Peng |
| author_facet | Jesús Eduardo Ochoa-Astorga Linni Wang Weiwei Du Yahui Peng |
| author_sort | Jesús Eduardo Ochoa-Astorga |
| collection | DOAJ |
| description | Fundus image registration is crucial in eye disease examination, as it enables the alignment of overlapping fundus images, facilitating a comprehensive assessment of conditions like diabetic retinopathy, where a single image’s limited field of view might be insufficient. By combining multiple images, the field of view for retinal analysis is extended, and resolution is enhanced through super-resolution imaging. Moreover, this method facilitates patient follow-up through longitudinal studies. This paper proposes a straightforward method for fundus image registration based on bifurcations, which serve as prominent landmarks. The approach aims to establish a baseline for fundus image registration using these landmarks as feature points, addressing the current challenge of validation in this field. The proposed approach involves the use of a robust vascular tree segmentation method to detect feature points within a specified range. The method involves coarse vessel segmentation to analyze patterns in the skeleton of the segmentation foreground, followed by feature description based on the generation of a histogram of oriented gradients and determination of image relation through a transformation matrix. Image blending produces a seamless registered image. Evaluation on the FIRE dataset using registration error as the key parameter for accuracy demonstrates the method’s effectiveness. The results show the superior performance of the proposed method compared to other techniques using vessel-based feature extraction or partially based on SURF, achieving an area under the curve of 0.526 for the entire FIRE dataset. |
| first_indexed | 2024-03-10T22:01:59Z |
| format | Article |
| id | doaj.art-11674c544bfc45bdad1d5f24397f55df |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T22:01:59Z |
| publishDate | 2023-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-11674c544bfc45bdad1d5f24397f55df2023-11-19T12:54:31ZengMDPI AGSensors1424-82202023-09-012318780910.3390/s23187809A Straightforward Bifurcation Pattern-Based Fundus Image Registration MethodJesús Eduardo Ochoa-Astorga0Linni Wang1Weiwei Du2Yahui Peng3Information and Human Science, Kyoto Institute of Technology University, Kyoto 6068585, JapanRetina & Neuron-Ophthalmology, Tianjin Medical University Eye Hospital, Tianjin 300084, ChinaInformation and Human Science, Kyoto Institute of Technology University, Kyoto 6068585, JapanSchool of Electronic and Information Engineering, Beijing Jiaotong University, Beijing 100044, ChinaFundus image registration is crucial in eye disease examination, as it enables the alignment of overlapping fundus images, facilitating a comprehensive assessment of conditions like diabetic retinopathy, where a single image’s limited field of view might be insufficient. By combining multiple images, the field of view for retinal analysis is extended, and resolution is enhanced through super-resolution imaging. Moreover, this method facilitates patient follow-up through longitudinal studies. This paper proposes a straightforward method for fundus image registration based on bifurcations, which serve as prominent landmarks. The approach aims to establish a baseline for fundus image registration using these landmarks as feature points, addressing the current challenge of validation in this field. The proposed approach involves the use of a robust vascular tree segmentation method to detect feature points within a specified range. The method involves coarse vessel segmentation to analyze patterns in the skeleton of the segmentation foreground, followed by feature description based on the generation of a histogram of oriented gradients and determination of image relation through a transformation matrix. Image blending produces a seamless registered image. Evaluation on the FIRE dataset using registration error as the key parameter for accuracy demonstrates the method’s effectiveness. The results show the superior performance of the proposed method compared to other techniques using vessel-based feature extraction or partially based on SURF, achieving an area under the curve of 0.526 for the entire FIRE dataset.https://www.mdpi.com/1424-8220/23/18/7809image registrationfundus imageretinal analysisblood vessel bifurcationregistration error |
| spellingShingle | Jesús Eduardo Ochoa-Astorga Linni Wang Weiwei Du Yahui Peng A Straightforward Bifurcation Pattern-Based Fundus Image Registration Method Sensors image registration fundus image retinal analysis blood vessel bifurcation registration error |
| title | A Straightforward Bifurcation Pattern-Based Fundus Image Registration Method |
| title_full | A Straightforward Bifurcation Pattern-Based Fundus Image Registration Method |
| title_fullStr | A Straightforward Bifurcation Pattern-Based Fundus Image Registration Method |
| title_full_unstemmed | A Straightforward Bifurcation Pattern-Based Fundus Image Registration Method |
| title_short | A Straightforward Bifurcation Pattern-Based Fundus Image Registration Method |
| title_sort | straightforward bifurcation pattern based fundus image registration method |
| topic | image registration fundus image retinal analysis blood vessel bifurcation registration error |
| url | https://www.mdpi.com/1424-8220/23/18/7809 |
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