Detection and measurement of retinal vessels in fundus images using amplitude modified second-order Gaussian filter

By Gang Luo, Opas Chutatape, Shankar Muthu Krishnan. IEEE Transactions on Biomedical Engineering, 2002

327 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/581c3eedd03dce2cde4140c6cae9d131cddd254c

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Citations in the corpus

An Ensemble Classification-Based Approach Applied to Retinal Blood Vessel Segmentation (2012, methodology): “…methodology exploits the piecewise linear approximation, the decrease in vessel diameter along vascular length, and the Gaussian-like intensity profile of retinal blood vessels and uses a kernel based on a Gaussian or its derivatives to enhance the vessel features in the retinal image [6], [8].”
Retinal vessel segmentation using the 2-D Gabor wavelet and supervised classification (2005, methodology): “In [2], [23], [24], matched filters are used to emphasize blood vessels.”
A Cross-Modality Learning Approach for Vessel Segmentation in Retinal Images (2016, background): “In profile models, the intensity of the vessel cross-section is considered as a Gaussian shape.”
An improved matched filter for blood vessel detection of digital retinal images (2007, methodology): “In this work, we use an optimization method to adjust the matched ﬁlter parameters with a hope that the performance of the matched ﬁlter will increase.”
Blood vessel segmentation methodologies in retinal images - A survey (2012, methodology): “The amplitude-modified second order Gaussian filter [48] has also been utilized for vessel detection.”
Retinal vascular image analysis as a potential screening tool for cerebrovascular disease: a rationale based on homology between cerebral and retinal microvasculatures (2005, methodology): “Other techniques of automated vessel width measurement have included the use of edge detection masks (Gonzalez & Woods, 1992) and sliding linear regression filters (Chapman et al. 2001; Gang et al. 2002).”
Retinal image analysis: concepts, applications and potential. (2006, methodology): “Other techniques of automated vessel width measurement have included the use of edge detection masks (Gonzalez and Woods, 1992) and sliding linear regression ﬁlters (Chapman et al., 2001; Gang et al., 2002).”
Ridge-based vessel segmentation in color images of the retina (2004, methodology): “The first group consists of rule-based methods and comprises vessel tracking [8]–[11], matched filter responses [1], [12], [13], grouping of edge pixels [14], model based locally adaptive thresholding [2], topology adaptive snakes [15] and morphology-based techniques [16]–[18].”
Trainable COSFIRE filters for vessel delineation with application to retinal images (2015, methodology): “Matched filtering techniques (Chauduri et al., 1989; Hoover et al., 2000; Gang et al., 2002; Al-Rawi et al., 2007) model the profile of the vessels by using a two-dimensional (2D) kernel with a Gaussian cross-section.”
An approach to localize the retinal blood vessels using bit planes and centerline detection (2012, methodology): “Chutatape and co-workers [42] evaluated the suitability amplitude-modified second order Gaussian filter whereas Zhang et al.”