Segmentation of Color Fundus Images of the Human Retina: Detection of the Optic Disc and the Vascular Tree Using Morphological Techniques

By Thomas Walter, Jean-Claude Klein. ISMDA, 2001

296 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/bbdaf482026e200a1c47e9b552c3ea810b0acece

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In Computer Vision and Pattern Recognition

In Radiology, Nuclear Medicine and Imaging

Citations in the corpus

A New Supervised Method for Blood Vessel Segmentation in Retinal Images by Using Gray-Level and Moment Invariants-Based Features (2011, methodology): “Other methods use mathematical morphology [15], [34]–[36] to benefit from a priori-known vasculature shape features, such as being piecewise linear and connected.”
Deep Retinal Image Understanding (2016, methodology): “Prior work on optic disc segmentation includes morphological operators [23] and hand crafted features [25] Morales et al. [13] use morphology along with Principal Component Analysis (PCA) to obtain the structure of the optic disk inside a retinal image.”
Optic Disc Detection From Normalized Digital Fundus Images by Means of a Vessels' Direction Matched Filter (2008, unknown): “from reaching the OD size, Walter and Klein [13] approximated the OD center as the center of the largest brightest connected object in a fundus image.”
Detecting the Optic Disc Boundary in Digital Fundus Images Using Morphological, Edge Detection, and Feature Extraction Techniques (2010, background): “On the other hand, although they do not belong to the two reviewed categories, works [22]–[24] proposed other relevant OD segmentation methods.”
Automatic detection of microaneurysms in color fundus images (2007, methodology): “These considerations have lead several authors to the use of KNN classifiers [32], [20], which consist in assigning to a new candidate z the class to which the majority among the K nearest neighbors of z belongs.”
A contribution of image processing to the diagnosis of diabetic retinopathy-detection of exudates in color fundus images of the human retina (2002, methodology): “• Automatic detection of features of the retina: The vascular tree [1], [7], [14]–[19], and the optic disc ([14], [17], [1], [20], [19]).”