RT Journal Article
T1 A new supervised method for blood vessel segmentation in retinal images by using gray-level and moment invariants-based features
A1 Marín Santos, Diego
A1 Gegúndez Arias, Manuel Emilio
A1 Bravo Caro, José Manuel
A1 Aquino Martín, Arturo
A2 Universidad de Huelva. Departamento de Ingeniería Electrónica, de Sistemas Informáticos y Automática
K1 Diagnóstico por imagenes
K1 Retinopatía diabética
K1 Imágenes, Tratamiento de las
AB This paper presents a new supervised method for blood vessel detection in digital retinal images. This method uses a neural network (NN) scheme for pixel classification and computes a 7-D vector composed of gray-level and moment invariants-based features for pixel representation. The method was evaluated on the publicly available DRIVE and STARE databases, widely used for this purpose, since they contain retinal images where the vascular structure has been precisely marked by experts. Method performance on both sets of test images is better than other existing solutions in literature. The method proves especially accurate for vessel detection in STARE images. Its application to this database (even when the NN was trained on the DRIVE database) outperforms all analyzed segmentation approaches. Its effectiveness and robustness with different image conditions, together with its simplicity and fast implementation, make this blood vessel segmentation proposal suitable for retinal image computer analyses such as automated screening for early diabetic retinopathy detection.
PB IEEE
SN 0278-0062
YR 2011
FD 2011-01
LK http://hdl.handle.net/10272/4447
UL http://hdl.handle.net/10272/4447
LA eng
NO Marín Santos, D., Aquini, A., Gegúndez Arias, M.E., Bravo Caro, J.M.: "A new supervised method for blood vessel segmentation in retinal images by using gray-level and moment invariants-based features". IEEE transactions on medical imaging, (2011, v. 30, n. 1, p. 146-158). ISSN 0278-0062
DS Repositorio Institucional de la Universidad de Huelva
RD 1 jun 2026