Automatic detection of microaneurysms in color fundus images

By Thomas Walter, Pascale Massin, Ali Erginay, Richard Ordonez, Clotilde Jeulin, Jean-Claude Klein. Medical Image Anal., 2007

401 citations recorded by the source corpus.

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

View Paper: https://www.semanticscholar.org/paper/f9b2818c1cd49c575902ff3917816349798665ea

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In Computer Vision and Pattern Recognition

In Ophthalmology

In Radiology, Nuclear Medicine and Imaging

Citations in the corpus

Algorithms for the Automated Detection of Diabetic Retinopathy Using Digital Fundus Images: A Review (2012, methodology): “Exudates are found using their high grey level variation, and their contours were determined by means of morphological reconstruction techniques [72].”
A New Supervised Method for Blood Vessel Segmentation in Retinal Images by Using Gray-Level and Moment Invariants-Based Features (2011, background): “Thus, blood containing elements in the retinal layer (such as vessels) are best represented and reach higher contrast in the green channel [56].”
Retinopathy Online Challenge: Automatic Detection of Microaneurysms in Digital Color Fundus Photographs (2010, methodology): “[20] described an alternative mathematical morphology based detection method.”
Identification and classification of microaneurysms for early detection of diabetic retinopathy (2013, methodology): “A morphological operations based technique for detecting candidate MAs was proposed by Walter et al. [11].”
TeleOphta: Machine learning and image processing methods for teleophthalmology (2013, neutral): “1.”
Artificial intelligence in retina. (2018, methodology): “KNNs have been successfully applied to segment vessels (GeethaRamani and Balasubramanian, 2018), exudates (Allam et al., 2017) and microaneurysms in CFP (Walter et al., 2007).”
Assessment of algorithms for mitosis detection in breast cancer histopathology images (2014, neutral): “In order to detect potential nuclei (candidates), a diameter closing operation (Walter et al., 2007) was applied to the median filtered image removing all dark structures with maximal extension smaller than a predefined parameter (the diameter was chosen to be 80 pixels).”
An Ensemble-Based System for Microaneurysm Detection and Diabetic Retinopathy Grading (2012, background): “MAs appearing near vessels become more easily detectable in this way.”