Title
Trainable COSFIRE filters for vessel delineation with application to retinal images
Author
Azzopardi, G.
Strisciuglio, N.
Vento, M.
Petkov, N.
Publication year
2015
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
Subject
Communication & Information
BIS - Business Information Services
TS - Technical Sciences
Biomedical Innovation
Biology
Healthy Living
COSFIRE
Delineation
Retinal image analysis
Trainable filters
Vessel segmentation
To reference this document use:
http://resolver.tudelft.nl/uuid:436ac092-cba2-46da-a9d4-651411920598
DOI
https://doi.org/10.1016/j.media.2014.08.002
TNO identifier
514736
Source
Medical Image Analysis, 19, 46-57
Bibliographical note
Available online 3 September 2014
Document type
article