An improved matched filter for blood vessel detection of digital retinal images

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A Discriminatively Trained Fully Connected Conditional Random Field Model for Blood Vessel Segmentation in Fundus Images (2017, methodology): “As Zana and Klein used different data to estimate the length of the structuring element, we selected l = 9 , which is consistent with the average caliber of DRIVE vessels as reported in [49].”
An approach to localize the retinal blood vessels using bit planes and centerline detection (2012, methodology): “Al-Rawi et al. [44] improved Chaudhuri's et al. [20] matched filter by using an exhaustive search optimization procedure.”
Multiscale AM-FM Methods for Diabetic Retinopathy Lesion Detection (2010, background): “[21], [22], and Match filters for vessel extraction [23], [24].”
An effective retinal blood vessel segmentation method using multi-scale line detection (2013, background): “Different variants [17] [18] [19] [20] have been proposed to improve the performance of the original matched filter response, ranging from the use of a threshold probing technique [17], 'double-sided' thresholding [19], or the first order derivative of Gaussian image [20] to provide a better…”
Blood vessel segmentation methodologies in retinal images - A survey (2012, methodology): “[50] 2007 Improved Gaussian matched filter Acc, AUC No L.”