Detection of Primary Glaucoma Using Fuzzy C Mean Clustering and Morphological Operators Algorithm

G. Pavithra, T. C. Manjunath () and Dharmanna Lamani
Additional contact information
G. Pavithra: VTU, RRC
T. C. Manjunath: DSCE, ECE
Dharmanna Lamani: SDMIT, ISE

A chapter in New Trends in Computational Vision and Bio-inspired Computing, 2020, pp 1407-1419 from Springer

Abstract: Abstract It is a well-known fact in the world that the glaucoma is the second largest disease which is affecting the human beings in the world. Proper care has to be taken to avoid this at an early stage as this would result in the loss of vision in the humans. This occurs due to the increase in the pressure in the eyes, where it bursts the nerve fibres leading to the vision loss. If the patient goes to the doctor, it is an expensive treatment. Hence, we are devising a low cost module method of detecting the primary glaucoma in the humans using their fundus images. The images of the patients will be taken by the fundus camera, analyzed and a info is given to the patient that he/she is affected with the disease. Once the person comes to know that they are affected, then proper diagnosis can be done by consultations from the hospital experts. The method of detecting the primary glaucoma is being presented in this section using a revised fuzzy-c means algorithm clubbed with morphological operators. CLAHE concepts are being used for the pre-processing and the edge detection is done using canny operator’s method. The segmentation is done using fuzzy and finally the region of interest, i.e., the cup and the disc areas are found out from which the ratio is computed, from where the disease can be detection seeing the ratio. The simulation results shown the effectivity of the method proposed by us in this research work.

Keywords: Modelsim; Matlab; Glaucoma; Eye; Disease; Normal; Affected; Blocks; Pressure; Tool; Hardware; Implementation; FPGA; Xilinx (search for similar items in EconPapers)
Date: 2020
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-030-41862-5_145

Ordering information: This item can be ordered from
http://www.springer.com/9783030418625

DOI: 10.1007/978-3-030-41862-5_145

Access Statistics for this chapter

More chapters in Springer Books from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().