Detection of retinal blood vessels from ophthalmoscope images using morphological approach
Dash, Jyotiprava (Veer Surendra Sai University of Technology (Burla, Índia))
Bhoi, Nilamani (Veer Surendra Sai University of Technology (Burla, Índia))

Fecha:	2017
Resumen:	Accurate segmentation of retinal blood vessels is an essential task for diagnosis of various pathological disorders. In this paper, a novel method has been introduced for segmenting retinal blood vessels which involves pre-processing, segmentation and post-processing. The pre-processing stage enhanced the image using contrast limited adaptive histogram equalization and 2D Gabor wavelet. The enhanced image is segmented using geodesic operators and a final segmentation output is obtained by applying a post-processing stage that involves hole filling and removal of isolated pixels. The performance of the proposed method is evaluated on the publicly available Digital retinal images for vessel extraction (DRIVE) and High-resolution fundus (HRF) databases using five different measurements and experimental analysis shows that the proposed method reach an average accuracy of 0. 9541 on DRIVE database and 0. 9568, 0. 9478 and 0. 9613 on HRF database with healthy, diabetic retinopathy (DR) and glaucomatous images respectively.
Derechos:	Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial i la comunicació pública de l'obra, sempre que no sigui amb finalitats comercials, i sempre que es reconegui l'autoria de l'obra original. No es permet la creació d'obres derivades.
Lengua:	Anglès
Documento:	Article ; recerca ; Versió publicada
Materia:	Retinal blood vessels ; Vessel segmentation ; Contrast limited adaptive histogram equalization ; Gabor wavelet transform
Publicado en:	ELCVIA : Electronic Letters on Computer Vision and Image Analysis, Vol. 16 Núm. 1 (2017) , p. 1-14 (Regular Issue) , ISSN 1577-5097

Adreça original: https://elcvia.cvc.uab.es/article/view/v16-n1-dash
Adreça alternativa: https://raco.cat/index.php/ELCVIA/article/view/321453
DOI: 10.5565/rev/elcvia.913

14 p, 1.1 MB

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