Zero-crossing interval correction in tracing eye-fundus blood vessels
Pattern Recognition
Level set methods: an overview and some recent results
Journal of Computational Physics
Level Set Evolution without Re-Initialization: A New Variational Formulation
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Automated Detection of Optic Disc Location in Retinal Images
CBMS '08 Proceedings of the 2008 21st IEEE International Symposium on Computer-Based Medical Systems
Extraction of retinal blood vessels by curvelet transform
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Fast localization of the optic disc using projection of image features
IEEE Transactions on Image Processing
Snakes, shapes, and gradient vector flow
IEEE Transactions on Image Processing
Gray and color image contrast enhancement by the curvelet transform
IEEE Transactions on Image Processing
A morphologic two-stage approach for automated optic disk detection in color eye fundus images
Pattern Recognition Letters
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Efficient optic disk (OD) localization and segmentation are important tasks in automated retinal screening. In this paper, we take digital curvelet transform (DCUT) of the enhanced retinal image and modify its coefficients based on the sparsity of curvelet coefficients to get probable location of OD. If there are not yellowish objects in retinal images or their size are negligible, we can then directly detect OD location by performing Canny edge detector to reconstructed image with modified coefficients. Otherwise, if the size of these objects is eminent, we can see circular regions in edge map as candidate regions for OD. In this case, we use some morphological operations to fill these circular regions and erode them to get final locations for candidate regions and remove undesired pixels in edge map. Since usually OD is surrounded by vessels, we choose the candidate region that has maximum summation of pixels in strongest edge map, which obtained by performing an appropriate threshold on the curvelet-based enhanced image, as final location of OD. Finally, the boundary of the OD is extracted by using level set deformable model. This method has been tested on different retinal image datasets and quantitative results are presented.