title: Trainable COSFIRE filters for vessel delineation with application to retinal images.
abstract: Retinal imaging provides a non-invasive opportunity for the diagnosis of several medical pathologies. The automatic segmentation of the vessel tree is an important pre-processing step which facilitates subsequent automatic processes that contribute to such diagnosis. We introduce a novel method for the automatic segmentation of vessel trees in retinal fundus images. We propose a filter that selectively responds to vessels and that we call B-COSFIRE with B standing for bar which is an abstraction for a vessel. It is based on the existing COSFIRE (Combination Of Shifted Filter Responses) approach. A B-COSFIRE filter achieves orientation selectivity by computing the weighted geometric mean of the output of a pool of Difference-of-Gaussians filters, whose supports are aligned in a collinear manner. It achieves rotation invariance efficiently by simple shifting operations. The proposed filter is versatile as its selectivity is determined from any given vessel-like prototype pattern in an automatic configuration process. We configure two B-COSFIRE filters, namely symmetric and asymmetric, that are selective for bars and bar-endings, respectively. We achieve vessel segmentation by summing up the responses of the two rotation-invariant B-COSFIRE filters followed by thresholding. The results that we achieve on three publicly available data sets (DRIVE: Se=0.7655, Sp=0.9704; STARE: Se=0.7716, Sp=0.9701; CHASE_DB1: Se=0.7585, Sp=0.9587) are higher than many of the state-of-the-art methods. The proposed segmentation approach is also very efficient with a time complexity that is significantly lower than existing methods.
authors.affiliation: Johann Bernoulli Institute for Mathematics and Computer Science, University of Groningen, The Netherlands.
authors.forename: George
authors.initials: G
authors.lastname: Azzopardi
authors.affiliation: Johann Bernoulli Institute for Mathematics and Computer Science, University of Groningen, The Netherlands; Dept. of Information and Electrical Engineering and Applied Mathematics (DIEM), University of Salerno, Italy.
authors.forename: Nicola
authors.initials: N
authors.lastname: Strisciuglio
authors.affiliation: Dept. of Information and Electrical Engineering and Applied Mathematics (DIEM), University of Salerno, Italy.
authors.forename: Mario
authors.initials: M
authors.lastname: Vento
authors.affiliation: Johann Bernoulli Institute for Mathematics and Computer Science, University of Groningen, The Netherlands.
authors.forename: Nicolai
authors.initials: N
authors.lastname: Petkov
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D005451:Fluorescein Angiography / Q000379:methods*; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D015588:Observer Variation; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012164:Retinal Diseases / Q000473:pathology*; D012171:Retinal Vessels / Q000473:pathology*; D012680:Sensitivity and Specificity
keywords: COSFIRE; Delineation; Retinal image analysis; Trainable filters; Vessel segmentation