Vessel network detection using contour evolution and color components
Automated retinal screening relies on vasculature segmentation before the identification of other anatomical structures of the retina. Vasculature extraction can also be input to image quality ranking, neovascularization detection and image registration. An extensive related literature often exclude...
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| Veröffentlicht in: | 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology 2010-01, Vol.2010, p.3129-3132 |
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| container_title | 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology |
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| creator | Ushizima, D M Medeiros, F N S Cuadros, J Martins, C I O |
| description | Automated retinal screening relies on vasculature segmentation before the identification of other anatomical structures of the retina. Vasculature extraction can also be input to image quality ranking, neovascularization detection and image registration. An extensive related literature often excludes the inherent heterogeneity of ophthalmic clinical images. The contribution of this paper consists in an algorithm using front propagation to segment the vessel network, including a penalty on the wait queue to the fast marching method, which minimizes leakage of the evolving boundary. The algorithm requires no manual labeling of seeds, a minimum number of parameters and it is capable of segmenting color ocular fundus images in real scenarios, where multi-ethnicity and brightness variations are parts of the problem. |
| doi_str_mv | 10.1109/IEMBS.2010.5626090 |
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Vasculature extraction can also be input to image quality ranking, neovascularization detection and image registration. An extensive related literature often excludes the inherent heterogeneity of ophthalmic clinical images. The contribution of this paper consists in an algorithm using front propagation to segment the vessel network, including a penalty on the wait queue to the fast marching method, which minimizes leakage of the evolving boundary. The algorithm requires no manual labeling of seeds, a minimum number of parameters and it is capable of segmenting color ocular fundus images in real scenarios, where multi-ethnicity and brightness variations are parts of the problem.</description><subject>Accuracy</subject><subject>Algorithms</subject><subject>Artificial Intelligence</subject><subject>Colorimetry - methods</subject><subject>Diabetic Retinopathy - pathology</subject><subject>Humans</subject><subject>Image color analysis</subject><subject>Image Enhancement - methods</subject><subject>Image Interpretation, Computer-Assisted - methods</subject><subject>Image segmentation</subject><subject>Pattern Recognition, Automated - methods</subject><subject>Pixel</subject><subject>Reproducibility of Results</subject><subject>Retina</subject><subject>Retinal Vessels - pathology</subject><subject>Retinopathy</subject><subject>Retinoscopy - methods</subject><subject>Sensitivity and Specificity</subject><issn>1094-687X</issn><issn>1557-170X</issn><issn>1558-4615</issn><isbn>1424441234</isbn><isbn>9781424441235</isbn><isbn>1424441242</isbn><isbn>9781424441242</isbn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2010</creationdate><recordtype>article</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><sourceid>EIF</sourceid><recordid>eNpFkNlOwzAQRc0musAPgITyAyneYz_SKkClIh5YxFvlxBMUSOwqTkD8PRYt8DKjuedqpHsROiN4RgjWl8v8bv4wozjeQlKJNd5DE8Ip55xQTvfRmAihUi6JOPgHjB9GgDVPpcpeRmgSwhvGFGNBjtGIRiIyrsYof4YQoEkc9J--e08s9FD2tXfJEGr3mpTe9X7oEvjwzfCjG2ej2vguznbjHbg-nKCjyjQBTnd7ip6u88fFbbq6v1kurlZpzbKsT5UykGlKpbIVq4gtbSZlZQtmGdMlNoLZUsqS4argJiauNLeaUWMLRQGrgk3RxfbvZihasOtNV7em-1r_5omG862hBoA_vOuNfQPb41wL</recordid><startdate>20100101</startdate><enddate>20100101</enddate><creator>Ushizima, D M</creator><creator>Medeiros, F N S</creator><creator>Cuadros, J</creator><creator>Martins, C I O</creator><general>IEEE</general><scope>6IE</scope><scope>6IH</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIO</scope><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope></search><sort><creationdate>20100101</creationdate><title>Vessel network detection using contour evolution and color components</title><author>Ushizima, D M ; 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| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | Accuracy Algorithms Artificial Intelligence Colorimetry - methods Diabetic Retinopathy - pathology Humans Image color analysis Image Enhancement - methods Image Interpretation, Computer-Assisted - methods Image segmentation Pattern Recognition, Automated - methods Pixel Reproducibility of Results Retina Retinal Vessels - pathology Retinopathy Retinoscopy - methods Sensitivity and Specificity |
| title | Vessel network detection using contour evolution and color components |
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