Geodesic Active Regions and Level Set Methods for Supervised Texture Segmentation
International Journal of Computer Vision
Mean Shift, Mode Seeking, and Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Semi-Automatic Identification of Optic Disk by Image Processing for Quantitative Funduscopy
SIBGRAPI '02 Proceedings of the 15th Brazilian Symposium on Computer Graphics and Image Processing
Gradient Vector Flow: A New External Force for Snakes
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
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
Pattern Recognition in Medical Imaging
Pattern Recognition in Medical Imaging
Extracting the Optic Disk Endpoints in Optical Coherence Tomography Data
WACV-MOTION '05 Proceedings of the Seventh IEEE Workshops on Application of Computer Vision (WACV/MOTION'05) - Volume 1 - Volume 01
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Object segmentation using graph cuts based active contours
Computer Vision and Image Understanding
Improving image segmentation by gradient vector flow and mean shift
Pattern Recognition Letters
Segmentation of Exudates and Optic Disk in Retinal Images
ICVGIP '08 Proceedings of the 2008 Sixth Indian Conference on Computer Vision, Graphics & Image Processing
Snakes, shapes, and gradient vector flow
IEEE Transactions on Image Processing
Automatic detection of optic disc from retinal fundus images using dynamic programming
ICIAR'12 Proceedings of the 9th international conference on Image Analysis and Recognition - Volume Part II
Mean shift based gradient vector flow for image segmentation
Computer Vision and Image Understanding
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Image segmentation plays an important role in the analysis of retinal images as the extraction of the optic disk provides important cues for accurate diagnosis of various retinopathic diseases. In recent years, gradient vector flow (GVF) based algorithms have been used successfully to successfully segment a variety of medical imagery. However, due to the compromise of internal and external energy forces within the resulting partial differential equations, these methods can lead to less accurate segmentation results in certain cases. In this paper, we propose the use of a new mean shift-based GVF segmentation algorithm that drives the internal/external energies towards the correct direction. The proposed method incorporates a mean shift operation within the standard GVF cost function to arrive at a more accurate segmentation. Experimental results on a large dataset of retinal images demonstrate that the presented method optimally detects the border of the optic disc.