研究目的
To develop an automated computer-aided diagnosis system for determining the vertical cup-to-disc ratio (VCDR) in retinal fundus images to assist in glaucoma screening.
研究成果
The proposed system achieves a mean VCDR difference of 0.11 compared to manual annotations, with good agreement for large VCDRs indicating pathology. It provides a reliable tool for glaucoma screening, with potential for improvement in cup segmentation for challenging cases.
研究不足
The system may struggle with images where the cup excavation is not obvious, such as in normal retinas, due to similar intensity values in the disc region. It is also affected by pathologies like parapapillary atrophy (PPA) that can interfere with accurate disc boundary determination.
1:Experimental Design and Method Selection:
The approach involves localization of the optic disc using trainable COSFIRE filters (vasculature-selective and disc-selective), delineation of the optic disc boundary by ellipse fitting, segmentation of the cup using Generalized Matrix Learning Vector Quantization (GMLVQ), and computation of VCDR.
2:Sample Selection and Data Sources:
Eight publicly available datasets (CHASEDB1, DIARETDB1, DRISHTI-GS1, DRIONS, DRIVE, HRF, MESSIDOR, ONHSD) with a total of 1712 retinal fundus images were used. Manual annotations by an ophthalmologist were provided for evaluation.
3:List of Experimental Equipment and Materials:
Retinal fundus images from specified datasets, COSFIRE filters, GMLVQ classifier, and preprocessing tools for image enhancement and vessel extraction.
4:Experimental Procedures and Operational Workflow:
Preprocessing (image rescaling, FOV mask generation, vessel extraction, inpainting), application of COSFIRE filters for optic disc localization, ellipse fitting for boundary delineation, GMLVQ for cup segmentation, and VCDR calculation.
5:Data Analysis Methods:
Performance evaluation using localization accuracy, relative height error (RHE) for disc and cup, VCDR error, Bland-Altman analysis, and ROC curves for classification.
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