Abstract
Cataract is a dulling or clouding of the lens inside the eye. Which is one of the most common diseases that might cause blindness. Considering the damage impact of cataract, we propose to use retinal vascular information for automatic cataract detection, which based on the classification of retinal image. This method focus on the preprocessing step of retinal image. Firstly, we use the maximum entropy method to enhance the contract level of fundus image. Next, in order to collect vessel information based on the Kirsh template of multi-layer filter is used. Last, adaptive weighted median filter has proposed to reduce the noise of the image. Then, according to the retinal blood vessel image, we extracted wavelet features, texture features for cataract classification. For each set of features, SVMs (support vector machines) is used for cataract classification. Finally, cataract image classified into normal, slight, medium or severe four-class. Through comparing the result of classification, three of four classes obtains the better accuracy than former. At the same time, the time that spend on feature extract is greatly reduced. The result demonstrate that our research on classification system is effective and has practical value.
The original version of this chapter was revised: The third and fourth authors’ affiliations were corrected. The erratum to this chapter is available at https://doi.org/10.1007/978-3-319-59858-1_24
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Acknowledgment
This research is supported by following grants: China National Key Research and Development Program with No. 2013BAH19F01.
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Dong, Y., Wang, Q., Zhang, Q., Yang, J. (2017). Classification of Cataract Fundus Image Based on Retinal Vascular Information. In: Xing, C., Zhang, Y., Liang, Y. (eds) Smart Health. ICSH 2016. Lecture Notes in Computer Science(), vol 10219. Springer, Cham. https://doi.org/10.1007/978-3-319-59858-1_16
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DOI: https://doi.org/10.1007/978-3-319-59858-1_16
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