Retinal Blood Vessel Extraction by Using Pre-processing and IterNet Model

Simple item page
dc.contributor.authorTenghongsakul, Kasi
dc.contributor.authorKanjanasurat, Isoon
dc.contributor.authorPurahong, Boonchana
dc.contributor.authorLasakul, Attasit
dc.date.accessioned2026-08-06T10:30:25Z
dc.date.available2026-08-06T10:30:25Z
dc.date.issued2020-12-03
dc.description.abstractAt present, many of visual disease happened from the abnormality of retinal vessels. The automatic vascular extraction from fundus images is essential for the diagnosis to reduce vision loss. This paper offers retinal blood vessel segmentation using the pre-processing and IterNet model, a convolution neural network. The green channel and gray scale image that is high contrast between the blood vessel and background, including the normalization, were used to improve blood vessel image quality. The proposed method was tested with two widely used databases, including DRIVE and CHASEDB-1, which unique characteristics in each data set. The results of blood vessel extraction of Drive and CHASEDB-1 achieved sensitivity 0.8126 and 0.7541, respectively.
dc.identifier.citation2020 24th International Computer Science and Engineering Conference Icsec 2020, 2020
dc.identifier.doi10.1109/ICSEC51790.2020.9375423
dc.identifier.other2-s2.0-85103452888
dc.identifier.urihttps://dspace.kmitl.ac.th/handle/123456789/11460
dc.source2020 24th International Computer Science and Engineering Conference Icsec 2020
dc.subjectDiabetic retinopathy
dc.subjectIterNet model
dc.subjectVessel extraction
dc.titleRetinal Blood Vessel Extraction by Using Pre-processing and IterNet Model
dc.typeConference Paper

Files

Collections

Research