Zero-crossing interval correction in tracing eye-fundus blood vessels
Pattern Recognition
Shape Modeling with Front Propagation: A Level Set Approach
IEEE Transactions on Pattern Analysis and Machine Intelligence
Comparison of Colour Spaces for Optic Disc Localisation in Retinal Images
ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 1 - Volume 1
Image Processing, Analysis, and Machine Vision
Image Processing, Analysis, and Machine Vision
Automated Optic Nerve Disc Parameterization
Informatica
Segmentation of optic disc in retinal images using an improved gradient vector flow algorithm
Multimedia Tools and Applications
Optic disk and cup boundary detection using regional information
ISBI'10 Proceedings of the 2010 IEEE international conference on Biomedical imaging: from nano to Macro
Robust optic disk segmentation from colour retinal images
Proceedings of the Seventh Indian Conference on Computer Vision, Graphics and Image Processing
Using blood vessels location information in optic disk segmentation
ICIAP'11 Proceedings of the 16th international conference on Image analysis and processing - Volume Part II
Precise segmentation of the optic disc in retinal fundus images
EUROCAST'11 Proceedings of the 13th international conference on Computer Aided Systems Theory - Volume Part I
A novel optic disc detection scheme on retinal images
Expert Systems with Applications: An International Journal
Superpixel classification based optic disc segmentation
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part II
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A deformable-model based approach is presented in this paper for robust detection of optic disk and cup boundaries. Earlier work on disk boundary detection up to now could not effectively solve the problem of vessel occlusion. The method proposed here improves and extends the original snake, which is essentially a deforming-only technique, in two aspects: knowledge-based clustering and smoothing update. The contour deforms to the location with minimum energy, and then self-clusters into two groups, i.e., edge-point group and uncertain-point group, which are finally updated by the combination of both local and global information. The modifications enable the proposed algorithm to become more accurate and robust to blood vessel occlusions, noises, ill-defined edges and fuzzy contour shapes. The comparative results on the 100 testing images show that the proposed method achieves better success rate (94%) when compared to those obtained by GVF-snake (12%) and modified ASM (82%). The proposed method is extended to detect the cup boundary and then extract the disk parameters for clinical application, which is a relatively new task in fundus image processing. The resulted cup-to-disk (C/D) ratio shows good consistency and compatibility when compared with the results from Heidelberg Retina Tomograph (HRT) under clinical validation.