Optic nerve head segmentation

By J. Lowell, Andrew Hunter, David H. Steel, A. Basu, R. Ryder, Eric Fletcher, Lee Kennedy. IEEE Transactions on Medical Imaging, 2004

447 citations recorded by the source corpus.

Reliable and efficient optic disk localization and segmentation are important tasks in automated retinal screening. General-purpose edge detection algorithms often fail to segment the optic disk due to fuzzy boundaries, inconsistent image contrast or missing edge features. This paper presents an algorithm for the localization and segmentation of the optic nerve head boundary in low-resolution images (about 20 /spl mu//pixel). Optic disk localization is achieved using specialized template matching, and segmentation by a deformable contour model. The latter uses a global elliptical model and a local deformable model with variable edge-strength dependent stiffness. The algorithm is evaluated against a randomly selected database of 100 images from a diabetic screening programme. Ten images were classified as unusable; the others were of variable quality. The localization algorithm succeeded on all bar one usable image; the contour estimation algorithm was qualitatively assessed by an ophthalmologist as having Excellent-Fair performance in 83% of cases, and performs well even on blurred images.

Subfields: Ophthalmology, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/2e153b498b756e0c6aed0c6d46c88dfd2bb6126b

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In Ophthalmology

In Radiology, Nuclear Medicine and Imaging

Citations in the corpus

Glaucoma risk index: Automated glaucoma detection from color fundus images (2010, methodology): “The performance of the proposed segmentation techniques strongly rely on proper initialization with the location of the ONH: proposed algorithms utilize ONH template-matching, intensity assumptions or the convergence of the vessel tree (Hoover and Goldbaum, 2003; Chrástek et al., 2005; Lowell et al.”
Retinal image analysis: concepts, applications and potential. (2006, methodology): “…element’’ (kernel) that has a Gaussian cross-proﬁle section, extruded or rotated into three dimensions to identify the cross-proﬁle of the blood vessel, which typically has a Gaussian or a Gaussian derivative proﬁle (Chaudhuri et al., 1989b; Hoover et al., 2000; Lowell et al., 2004b) (Fig.”
Joint Optic Disc and Cup Segmentation Based on Multi-Label Deep Network and Polar Transformation (2018, background): “contour model [19] to detect the contour based on image gradient.”
Optic Disk and Cup Segmentation From Monocular Color Retinal Images for Glaucoma Assessment (2011, unknown): “This is achieved either by employing a preprocessing step [32] or by constraining the segmentation result to a circular or elliptically shaped object [35], [36].”
Detecting the Optic Disc Boundary in Digital Fundus Images Using Morphological, Edge Detection, and Feature Extraction Techniques (2010, methodology): “[18] also localized the OD by means of template matching as well as also selected a deformable contour model for its segmentation.”
RIM-ONE: An open retinal image database for optic nerve evaluation (2011, background): “The ONHSD [12], from the same group, is a dataset that contains 100 retinal fundus images and four ONH edges delimited by four clinicians.”
CE-Net: Context Encoder Network for 2D Medical Image Segmentation (2019, methodology): “been proposed and adopted, such as mathematical morphology, global elliptical model, local deformable model [28], and modified active shape model [29].”
Optic Disc Detection From Normalized Digital Fundus Images by Means of a Vessels' Direction Matched Filter (2008, methodology): “[5] carefully designed an OD template which was then correlated to the intensity component of the fundus image using the full Pearson-R correlation.”
REFUGE Challenge: A Unified Framework for Evaluating Automated Methods for Glaucoma Assessment from Fundus Photographs (2019, unknown): “ONHSD2 (Lowell et al., 2004) and DRIONS-DB3 (Carmona et al., 2008) only include segmentations of the OD, and no glaucoma labels are given.”
An Active Contour Model for Segmenting and Measuring Retinal Vessels (2009, background): “If the growing centerline reaches the image edge, extends further than (where is the Optic Nerve Head radius, estimated using the algorithm reported in [28]), or there is no segment profile center point within the radius , then the SE is classified as a “leaf.”