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Optic cup segmentation from fundus images for glaucoma diagnosis
Man Hu1, Chenghao Zhu2, Xiaoxing Li2
1a National Key Discipline of Pediatrics, Ministry of Education , Department of Ophthalmology , Beijing Children's Hospital, Capital Medical University , Beijing , China.
Bioengineered
|October 21, 2016
Summary
This study introduces an advanced method for automatically segmenting the optic cup in fundus images, crucial for early glaucoma diagnosis. The technique improves accuracy by analyzing color and vessel data, aiding in the detection of this sight-threatening disease.
Area of Science:
- Ophthalmology
- Medical Imaging
- Computer Vision
Background:
- Glaucoma is a leading cause of irreversible blindness, with early detection being challenging.
- Computer-aided diagnosis systems are advancing glaucoma screening.
- Accurate optic cup segmentation in fundus images is vital for glaucoma assessment.
Purpose of the Study:
- To develop an automated method for optic cup segmentation in fundus images.
- To enhance the accuracy and reliability of glaucoma screening tools.
Main Methods:
- A novel algorithm utilizing color difference and vessel bends information from fundus images.
- Incorporation of confidence levels for information points to refine boundary determination.
- Evaluation on a public fundus image database.
Main Results:
- The proposed method achieved more consistent optic cup boundary segmentation compared to existing techniques.
- Experimental results showed improved agreement with ophthalmologist assessments.
Conclusions:
- The developed automatic optic cup segmentation method shows significant promise for improving glaucoma diagnosis.
- The integration of color and vessel bend information with confidence evaluation enhances segmentation accuracy.
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