Adaptive Local Thresholding by Verification-Based Multithreshold Probing with Application to Vessel Detection in Retinal Images

By Xiaoyi Jiang, Daniel Mojon. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2003

734 citations recorded by the source corpus.

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

View Paper: https://www.semanticscholar.org/paper/4c244a115f575a158ed8a898bd8db0c4cc921868

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In Computer Vision and Pattern Recognition

In Radiology, Nuclear Medicine and Imaging

Citations in the corpus

Hierarchical retinal blood vessel segmentation based on feature and ensemble learning (2015, neutral): “Jiang [33] 2003 N.”
Segmentation of retinal blood vessels by combining the detection of centerlines and morphological reconstruction (2006, methodology): “The green channel is considered in several works [5], [23], [ 31 ], [34] as the natural basis for vessel segmentation because it normally presents a higher contrast between vessels and retinal background.”
An Active Contour Model for Segmenting and Measuring Retinal Vessels (2009, methodology): “Local detection methods include adaptive local thresholding [4] and the use of 2-”
Retinal Vessel Centerline Extraction Using Multiscale Matched Filters, Confidence and Edge Measures (2006, methodology): “This is difficult even with adaptive thresholds [31], [32] due to non-uniform illumination and the presence of pathologies.”
Robust Vessel Segmentation in Fundus Images (2013, background): “…0.9600 0.9325 Chaudhuri et al. [4] 0.6168 0.9741 0.9284 Al-Diri and Hunter [22] — — 0.9258 Espona et al. (pixel accuracy) [41] 0.6615 0.9575 0.9223 Jiang and Mojon [42] — — 0.9212 All background — — 0.8727 “—” indicates that this information was not available. high resolution fundus database [33].”
Ridge-based vessel segmentation in color images of the retina (2004, methodology): “[2] and A = 0:759 for the algorithm by Hoover et al.”
An effective retinal blood vessel segmentation method using multi-scale line detection (2013, methodology): “Jiang et al. [28] 0.9222 0.6915 – – Hoover et al. [17] – – 0.9240 0.6998 Perez et al. [30] 0.9316 0.7670 0.9196 0.7607 Zana et al. [27] 0.9320 0.7318 – – Chaudhuri et al. [16] 0.8884 0.5587 – – Ricci-line [8] 0.”
Retinal Blood Vessel Segmentation Using Line Operators and Support Vector Classification (2007, methodology): “Several methods have been proposed in the literature to address these problems including matched filtering [7], tracking methods [8]–[10], multithreshold probing [11], mathematical morphology [12], or a combination of nonlinear filtering and morphological operations [13].”
Ilastik: Interactive learning and segmentation toolkit (2011, methodology): “[14], and the ridge-based segmentation proposed by Staal et al.”
An approach to localize the retinal blood vessels using bit planes and centerline detection (2012, methodology): “The matched filter (MF) was first proposed by Chaudhuri et al. [20] and later adapted and extended by Hoover et al. [37], Xiaoyi and Mojon [38] and Yao and Chen [39].”