Abstract
This study has developed a novel saliency detection method based on compactness feature for detecting three common types of leakage in retinal fluorescein angiogram: large focal, punctate focal, and vessel segment leakage. Leakage from retinal vessels occurs in a wide range of retinal diseases, such as diabetic maculopathy and paediatric malarial retinopathy. The proposed framework consists of three major steps: saliency detection, saliency refinement and leakage detection. First, the Retinex theory is adapted to address the illumination inhomogeneity problem. Then two saliency cues, intensity and compactness, are proposed for the estimation of the saliency map of each individual superpixel at each level. The saliency maps at different levels over the same cues are fused using an averaging operator. Finally, the leaking sites can be detected by masking the vessel and optic disc regions. The effectiveness of this framework has been evaluated by applying it to different types of leakage images with cerebral malaria. The sensitivity in detecting large focal, punctate focal and vessel segment leakage is 98.1, 88.2 and 82.7 %, respectively, when compared to a reference standard of manual annotations by expert human observers. The developed framework will become a new powerful tool for studying retinal conditions involving retinal leakage.
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Acknowledgments
The project was supported by National Basic Research Program of China (2013CB328806), National Natural Science Foundation of China (81430039, 61572076), the Key Projects in the National Science & Technology Pillar Program (2013BAI01B01), National Hi-Tech Research and Development Program (2015AA043203), and China Postdoctoral Science Foundation Grant (2015M570940), the Key Laboratory of Photoelectronic Imaging Technology and System Beijing Institute of Technology Ministry of Education of China (2016OEIOF03); the Beijing Institute of Technology Research Fund Program for Young Scholars.
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Zhao, Y., Su, P., Yang, J. et al. A compactness based saliency approach for leakages detection in fluorescein angiogram. Int. J. Mach. Learn. & Cyber. 8, 1971–1979 (2017). https://doi.org/10.1007/s13042-016-0573-4
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DOI: https://doi.org/10.1007/s13042-016-0573-4