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| rdf:type
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| Description
| - This paper proposes an automatic localization and boundary detection of retina images using basic filters to support ophthalmologists for detection and diagnoses eyes harmful diseases such as glaucoma and diabetic retinopathy accurately and diligently. The proposed system comprising three main phases including preprocessing, segmentation and detection phase. The preprocessing phase is used to enhance retinal image and to remove the noise of the retina image. The second phase is the segmentation for main parts of retinal image including optic disc, blood vessels, and fovea to extract their features. Optic disc is segmented using color intensity, the region of interest (ROI) is detected and morphological operations are applied to reduce search complexity. Also, fovea feature is extracted and the blood vessels tree is extracted from retinal image using line detection techniques. The third phase is the detection, in which identification and classifying whether the input image is left or right eye, to support ophthalmologists in identifying which eye is infected by the disease and to check it periodically. Basic image processing filters including average filter, median filter, spatial filter and morphological filter are used in all system phases. Moreover, a simple approach were used to detect left and right retinal fundus images. The proposed system is tested and evaluated using a subset of ophthalmologic images of the publically available DRIVE database. 2013 Springer-Verlag.
- This paper proposes an automatic localization and boundary detection of retina images using basic filters to support ophthalmologists for detection and diagnoses eyes harmful diseases such as glaucoma and diabetic retinopathy accurately and diligently. The proposed system comprising three main phases including preprocessing, segmentation and detection phase. The preprocessing phase is used to enhance retinal image and to remove the noise of the retina image. The second phase is the segmentation for main parts of retinal image including optic disc, blood vessels, and fovea to extract their features. Optic disc is segmented using color intensity, the region of interest (ROI) is detected and morphological operations are applied to reduce search complexity. Also, fovea feature is extracted and the blood vessels tree is extracted from retinal image using line detection techniques. The third phase is the detection, in which identification and classifying whether the input image is left or right eye, to support ophthalmologists in identifying which eye is infected by the disease and to check it periodically. Basic image processing filters including average filter, median filter, spatial filter and morphological filter are used in all system phases. Moreover, a simple approach were used to detect left and right retinal fundus images. The proposed system is tested and evaluated using a subset of ophthalmologic images of the publically available DRIVE database. 2013 Springer-Verlag. (en)
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| Title
| - Automatic localization and boundary detection of retina in images using basic image processing filters
- Automatic localization and boundary detection of retina in images using basic image processing filters (en)
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| skos:prefLabel
| - Automatic localization and boundary detection of retina in images using basic image processing filters
- Automatic localization and boundary detection of retina in images using basic image processing filters (en)
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| skos:notation
| - RIV/61989100:27240/11:86085015!RIV13-MSM-27240___
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| http://linked.open...avai/predkladatel
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| http://linked.open...avai/riv/aktivita
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| http://linked.open...avai/riv/aktivity
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| http://linked.open...vai/riv/dodaniDat
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| http://linked.open...aciTvurceVysledku
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| http://linked.open.../riv/druhVysledku
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| http://linked.open...iv/duvernostUdaju
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| http://linked.open...titaPredkladatele
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| http://linked.open...dnocenehoVysledku
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| http://linked.open...ai/riv/idVysledku
| - RIV/61989100:27240/11:86085015
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| http://linked.open...riv/jazykVysledku
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| http://linked.open.../riv/klicovaSlova
| - Morphological filters; Line detection; Input image; Image processing filters; Fundus image; Diabetic retinopathy; Detection phase; Detection and diagnosis; Color intensity; Boundary detection; Average filter; Automatic localization (en)
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| http://linked.open.../riv/klicoveSlovo
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| http://linked.open...ontrolniKodProRIV
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| http://linked.open...v/mistoKonaniAkce
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| http://linked.open...i/riv/mistoVydani
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| http://linked.open...i/riv/nazevZdroje
| - Advances in Intelligent Systems and Computing. Volume 179
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| http://linked.open...in/vavai/riv/obor
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| http://linked.open...ichTvurcuVysledku
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| http://linked.open...cetTvurcuVysledku
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| http://linked.open...UplatneniVysledku
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| http://linked.open...iv/tvurceVysledku
| - Hassanien, A. E.
- Platoš, Jan
- Snášel, Václav
- Soliman, O. S.
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| http://linked.open...vavai/riv/typAkce
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| http://linked.open.../riv/zahajeniAkce
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| issn
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| number of pages
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| http://bibframe.org/vocab/doi
| - 10.1007/978-3-642-31603-6_15
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| http://purl.org/ne...btex#hasPublisher
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| https://schema.org/isbn
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| http://localhost/t...ganizacniJednotka
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| is http://linked.open...avai/riv/vysledek
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