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Author: search.filters.author.Kanjanasurat, IsoonSubject: search.filters.subject.Diabetic retinopathy

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    Retinal Blood Vessel Extraction by Using Pre-processing and IterNet Model
    (2020-12-03)
    Tenghongsakul, Kasi
    ;
    Kanjanasurat, Isoon
    ;
    Purahong, Boonchana
    ;
    Lasakul, Attasit
    At 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.
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    Blood Vessel Extraction and Optic Disk Localization for Diabetic Retinopathy
    (2020-09-15)
    Kanjanasurat, Isoon
    ;
    Purahong, Boonchana
    ;
    Pintavirooj, Chuchart
    ;
    Satayarak, Nitjaree
    ;
    Benjangkaprasert, Chawalit
    This paper presents methods of vascular extraction and optic disk localization in the retinal images. Our approach begins with preprocessing to improve the quality of blood vessels. In the next step, a matrix filter was applied to express blood vessels. Finally, the blood vessel structure was used to estimate the location of the optic disk. The proposed method was tested on all different forty retinal images from the DRIVE database, which public retinal image dataset. The results of vessel extraction were compared with the ground truth image. The error of vascular extraction showed that the average sensitivity and accuracy were 79.81% and 94.98%, respectively. The optic disk localization achieved 97.5%.