Intelligent Decision Support Systems
Applications in Signal Processing
Ed. by Borra, Surekha / Dey, Nilanjan / Bhattacharyya, Siddhartha / Bouhlel, Mohamed Salim
- eBook (PDF)
- Publication Date:
- October 2019
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5. Intelligent approach for retinal disease identification
Rajyaguru, Vipul C. / Vithalani, Chandresh H. / Thanki, Rohit M.
In recent time, various types of biomedical images are available at every health stations for diagnostic of different diseases. These images can easily be detected and can identify the issues of the disease by analyzing it. Nowadays, retinalbased images are widely used for the identification of diabetes-related health issues. Glaucoma is a retinal disease that plays an important role in detecting of an earlier stage of diabetes. This disease affects the optic nerve system and astrocytes of the retina. This chapter presents basic steps for the detection of glaucoma in the retinal image. In this approach, various retinal features such as nerve lines, optic cup, optic disk, cup-to-disk ratio, and so on are used for the detection of glaucoma disease using the retinal image. In this chapter, a study of various approaches for glaucoma detection based on different image-processing methodologies and machine-learning algorithms are given. This chapter also provides an analysis of glaucoma detection in color fundus retinal image using various image-processing methods, fuzzy C-mean clustering, and thresholding. This approach can be used for the classification of the color retinal image. Experimental results also show that the presented approach works better than the existing approaches in the literature.
Vipul C. Rajyaguru, Chandresh H. Vithalani, Rohit M. Thanki (2019). 5. Intelligent approach for retinal disease identification. In Surekha Borra, Nilanjan Dey, Siddhartha Bhattacharyya, Mohamed Salim Bouhlel (Eds.), Intelligent Decision Support Systems: Applications in Signal Processing (pp. 99–129). Berlin, Boston: De Gruyter. https://doi.org/10.1515/9783110621105-005
Book DOI: https://doi.org/10.1515/9783110621105
Online ISBN: 9783110621105© 2019 Walter de Gruyter GmbH, Berlin/Munich/Boston