Abstract
The Retinal Blood Vessel segmentation plays a vital role in automatic retinal disease screening systems. It helps in the screen process of glaucoma, diabetic retinopathy, and other eye- related diseases. The primary objective of this paper is to consequently segment the vessels in fundus retinal pictures, which encourages us in diabetic retinopathy screening. The initial enhancement of image is carried out using Histogram Equalization. After which, the green channel of the image is applied with morphological image processing to remove the optic disc. The image segmentation is then performed to modify the intensity of contrast and little pixels viewed as noise are evacuated. The obtained image would represent the blood vessels of the original image. This paper proposes an expected maximization algorithm to segment the blood vessels in the human retina. The novelty of these method is to perform uniform intensity distribution in retinal images. The proposed method was tested by publicly available datasets such as DRIVE, STARE, MESSIDOR, DIARETDB0, and University of Lincoln. The proposed method has obtained an average area under receiver operating characteristics of 0.9203. Moreover, this shows a better performance than other state-of-the-art methods.
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Murugan, R. (2020). The Retinal Blood Vessel Segmentation Using Expected Maximization Algorithm. In: Gupta, M., Konar, D., Bhattacharyya, S., Biswas, S. (eds) Computer Vision and Machine Intelligence in Medical Image Analysis. Advances in Intelligent Systems and Computing, vol 992. Springer, Singapore. https://doi.org/10.1007/978-981-13-8798-2_6
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DOI: https://doi.org/10.1007/978-981-13-8798-2_6
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