Automated microaneurysm detection using local contrast normalization and local vessel detection

By A. Fleming, S. Philip, K. Goatman, J. Olson, P. Sharp. IEEE Transactions on Medical Imaging, 2006

363 citations recorded by the source corpus.

Subfields: Ophthalmology, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/906d00fef675d0ceec4ec66353e3a305f913043b

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Ophthalmology

1. Michael David Abràmoff
2. Meindert Niemeijer
3. B. van Ginneken
4. Joachim Hornegger
5. András Hajdu
6. Bálint Antal
7. Renzo Perfetti
8. Elisa Ricci

In Radiology, Nuclear Medicine and Imaging

1. B. van Ginneken
2. Michael David Abràmoff
3. Meindert Niemeijer
4. Hiroshi Fujita
5. Joachim Hornegger
6. Woong Bae
7. Ana Maria Mendonça
8. Jirí Jan

Citations in the corpus

• Retinopathy Online Challenge: Automatic Detection of Microaneurysms in Digital Color Fundus Photographs (2010, methodology): “Additionally, a number of papers reported the result of an receiver operating characteristic (ROC) analysis [25] on a per image basis [18], [19], [21].”
• Identification and classification of microaneurysms for early detection of diabetic retinopathy (2013, methodology): “A local contrast normalization based method was proposed in [13].”
• An Ensemble-Based System for Microaneurysm Detection and Diabetic Retinopathy Grading (2012, methodology): “While the original paper [15] is written to detect MAs on fluorescein angiographic images, our implementation is based on the modified version published by Fleming et al. [16].”
• IDRiD: Diabetic Retinopathy - Segmentation and Grading Challenge. (2020, methodology): “…involved for lesion segmentation are mathematical morphology ( Joshi and Karule, 2019; Hatanaka et al., 2008; Zhang et al., 2014 ), region growing ( Fleming et al., 2006; Li and Chutatape, 2004 ), and supervised methods ( Wu et al., 2017; Zhou et al., 2017; Garcia et al., 2009; Tang et al., 2013 ).”
• Automatic Diabetic Macular Edema Detection in Fundus Images Using Publicly Available Datasets (2011, methodology): “This approach has been used previously (Niemeijer et al., 2005; Fleming et al., 2006) and has considerable computation performance advantages over other methods (Foracchia et al.”
• Optimal Wavelet Transform for the Detection of Microaneurysms in Retina Photographs (2008, methodology): “The two previous approaches have been combined, thus [13], [7], [9] or [14] propose hierarchical classifiers that first perform a pixel-wise classification to extract candidate lesions and then a statistical classifier was used to reject.”
• Retinal vessel segmentation by improved matched filtering: evaluation on a new high-resolution fundus image database (2013, background): “Several pathologies affecting the retinal vascular structures due to diabetic retinopathy (DR; neovascular nets, haemorrhages, microaneurysms) can be found in fundus images using precisely segmented blood vessels [4].”
• DIARETDB 1 diabetic retinopathy database and evaluation protocol (2007, methodology): “[6] had similar approach for microaneurysms, but the green channel of the original fundus image was divided with the background intensity image.”
• The DIARETDB1 Diabetic Retinopathy Database and Evaluation Protocol (2007, background): “1(c) appear because of the increased permeability of the capillary walls.”