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ORIGINAL ARTICLE
Year : 2020  |  Volume : 10  |  Issue : 2  |  Page : 76-85

Forming optimal projection images from intra-retinal layers using curvelet-based image fusion method


1 Medical Physics and Biomedical Engineering Unit, Ophthalmic Research Center, Shahid Beheshti University of Medical Sciences, Tehran; Medical Image and Signal Processing Research Center, Isfahan University of Medical Sciences, Isfahan, Iran
2 Medical Image and Signal Processing Research Center, Isfahan University of Medical Sciences, Isfahan, Iran
3 Department of Ophthalmology, School of Medicine, Isfahan University of Medical Sciences, Isfahan, Iran

Correspondence Address:
Hossein Rabbani
Medical Image and Signal Processing Research Center, Isfahan University of Medical Sciences, Isfahan
Iran
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Source of Support: None, Conflict of Interest: None


DOI: 10.4103/jmss.JMSS_43_19

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Background: Image fusion is the process of combining the information of several input images into one image. Projection images obtained from three-dimensional (3D) optical coherence tomography (OCT) can show inlier retinal pathology and abnormalities that are not visible in conventional fundus images. In recent years, the projection image is often made by an average on all retina that causes to lose many intraretinal details. Methods: In this study, we focus on the formation of optimum projection images from retinal layers using Curvelet-based image fusion. The latter consists of three main steps. In the earlier studies, macular spectral 3D data using diffusion map-based OCT were segmented into 12 different boundaries identifying 11 retinal layers in three dimensions. In the second step, projection images are attained using conducting some statistical methods on the space between each pair of boundaries. In the next step, retinal layers are merged using Curvelet transform to make the final projection images. Results: These images contain integrated retinal depth information as well as an ideal opportunity to better extract retinal features such as vessels and the macula region. Finally, qualitative and quantitative evaluations show the superiority of this method to the average-based and wavelet-based fusion methods. Overall, our method obtains the best results for image fusion in all terms such as entropy (6.7744) and AG (9.5491). Conclusion: Creating an image with more and detailed information made by the Curvelet-based image fusion has significantly higher contrast. There are also many thin veins in Curvelet-based fused image, which are absent in average-based and wavelet-based fused images.


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