Automated feature extraction in color retinal images by a model based approach

By Huiqi Li, Opas Chutatape. IEEE Transactions on Biomedical Engineering, 2004

646 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/1d5bb74e730956550c22a3b984f26bf9cfe80f2c

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In Computer Vision and Pattern Recognition

In Ophthalmology

In Radiology, Nuclear Medicine and Imaging

Citations in the corpus

Segmentation of retinal blood vessels by combining the detection of centerlines and morphological reconstruction (2006, background): “Other indirect applications include automatic generation of retinal maps for the treatment of age-related macular degeneration [15], extraction of characteristic points of the retinal vasculature for temporal or multimodal image registration [16]–[18], retinal image mosaic synthesis [19], identifica”
Automatic Diabetic Macular Edema Detection in Fundus Images Using Publicly Available Datasets (2011, methodology): “In the literature, there are exudate segmentation algorithms that are evaluated on an image based fashion in order to diagnose DME (Osareh et al., 2003; Walter et al., 2002; Li and Chutatape, 2004; Niemeijer et al., 2007; Sanchez et al., 2009; Garcia et al., 2009).”
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 ).”
Optic disc and cup segmentation methods for glaucoma detection with modification of U-Net convolutional neural network (2017, methodology): “Some methods that are not mentioned in this section, such as [5, 18, 19], have mostly been evaluated either on datasets that are not currently publicly available, or on very small datasets, or used metrics dependent on proportion between classes, thus making it harder to compare with them.”
A New Supervised Method for Blood Vessel Segmentation in Retinal Images by Using Gray-Level and Moment Invariants-Based Features (2011, background): “On the other hand, the vascular tree can also be useful as valuable information to locate other fundus features such as the optic disc [24]–[26] and the fovea [27].”
DIARETDB 1 diabetic retinopathy database and evaluation protocol (2007, positive): “In hemorrhage detection, Zhang and Chutape [25] used histogram specification applied to each individual RGB color component to normalize the colors between different fundus images.”
Retinal image analysis: concepts, applications and potential. (2006, methodology): “Li and Chutatape (2004) produced a training set using the brightest pixels that were ﬁrstly clustered as candidate optic disc regions.”
An approach to localize the retinal blood vessels using bit planes and centerline detection (2012, background): “retinal image mosaic synthesis [11], optic disc identification and fovea localization [12].”