Vessel transform for automatic optic disk detection in retinal images. Issue 9 (1st September 2015)

• Record Type: Journal Article
• Authors: Muangnak, Nittaya; Aimmanee, Pakinee; Makhanov, Stanislav; Uyyanonvara, Bunyarit
• Is Part Of: IET image processing. Volume 9:Issue 9(2015)
• Page Start: 743
• Page End: 750
• Publication Date: 2015-09-01
• Subjects: eye -- medical image processing -- blood vessels -- transforms -- fuzzy set theory -- biomedical optical imaging -- convergence of numerical methods; vessel transform -- automatic optic disk detection -- retinal images -- ophthalmic image processing -- computerised solution -- numerical algorithms -- low‐quality images -- blood vessels -- vessel clustering -- scale‐space analysis -- integrated method -- SS algorithm -- fuzzy convergence method -- FC method -- circular transform -- SSVT sensitivity; Image processing -- Periodicals; 621.36705
• DOI: 10.1049/iet-ipr.2015.0030
• Languages: English
• ISSNs: 1751-9659

Abstract

Abstract : Precise localisation of an optic disk (OD) in the retinal images is one of the most important problems in the ophthalmic image processing. Although a considerable progress has been made towards a computerised solution of the problem, the numerical algorithms often fail on retinal images characterised by poor quality. Therefore, the authors propose a new method suitable for low‐quality images based on exploiting the convergence of the blood vessels to the OD. The novelty of the proposed techniques includes clustering the vessels endowed with a novel correction procedure and the vessel transform (VT) which measures the distance to the main clusters. The algorithm is integrated into the scale‐space (SS) analysis to detect the boundary of the OD. The integrated method is called SS algorithm with VT (SSVT). SSVT has been tested on retinal images from two databases with fair and poor images against the fuzzy convergence (FC) method and a modification of the circular transform proposed by Lu. The absolute improvement on sensitivity of SSVT against FC and Lu's are up to 12.37% and 8.18%. Bigger improvements of SSVT in terms of positive predictive value are up to 37.46% and 30.84% against FC and Lu's, respectively.