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Updated: Jan 20, 2026

Optical Sectioning and Visualization of the Intervertebral Disc from Embryonic Development to Degeneration
Published on: July 8, 2021
A new and effective method for human retina optic disc segmentation with fuzzy clustering method based on active
Ahmad S Abdullah1,2, Javad Rahebi3, Yasa Ekşioğlu Özok4
1Altinbas University, Istanbul, Turkey. Ahmed.alogaidi28@gmail.com.
A novel fuzzy clustering and active contour model approach accurately detects and segments optic discs in retinal images. This method achieves high accuracy across multiple datasets, outperforming existing techniques for diagnosing eye conditions.
Area of Science:
- Ophthalmology
- Medical Imaging
- Computer Vision
Background:
- Accurate localization and segmentation of the optic disc are crucial for diagnosing various retinal diseases.
- Existing methods often struggle with variations in image quality and pathological features.
Purpose of the Study:
- To propose a novel, robust approach for optic disc localization and segmentation in human retinal images.
- To evaluate the performance of the proposed method on diverse retinal imaging databases containing abnormalities.
Main Methods:
- Utilizing an initial fuzzy clustering means algorithm to identify the optic disc boundary.
- Employing active contour model evolution guided by a fuzzy clustering algorithm for precise segmentation.
Main Results:
- Achieved 100% detection accuracy on DRIVE, DIARETDB1, and DRIONS-DB databases.
- Demonstrated high segmentation accuracy (97.01-99.46%) and overlap (78.35-84.56%) across four databases.
- Showcased superior performance compared to existing methods in literature.
Conclusions:
- The proposed fuzzy clustering and active contour model approach offers a highly accurate and robust solution for optic disc analysis.
- This method holds significant potential for improving the early detection and monitoring of retinal diseases.
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