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001-es BibID:	BIBFORM009763
Első szerző:	Harangozó Roland
Cím:	Subsampling strategies to improve learning-based retina vessel segmentation / Roland Harangozó, Péter Veres, András Hajdu
Dátum:	2009
Megjegyzések:	The proper segmentation of the vascular system of the retina has a very important role in automatic screening systems. Its detection helps the localization of other anatomical parts and also the detection of possible vascular disorders. State-of-the-art machine learning algorithms are reported to have good performance in this field. However, with the spatial resolution of the fundus images growing, it is necessary to decrease the number of training pixels to save computations. In this paper, we investigate several subsampling strategies with the motivation to find the best segmentation results with involving fewer pixels into the analyses. Besides checking the computational advantages, we demonstrate how the segmentation accuracy drops with the level of subsampling.
Tárgyszavak:	Műszaki tudományok Informatikai tudományok konferenciacikk Centroidal Voronoi tessellations Retinal screening Subsampling Vessel segmentation Automatic screening Centroidal Voronoi tessellations Computational advantages Fundus image Machine learning algorithms Retina vessels Segmentation accuracy Segmentation results Spatial resolution Vascular system Vessel segmentation Image processing Imaging systems Ophthalmology Pixels Learning algorithms
Megjelenés:	IEEE 16th International Conference on Image Processing (ICIP2009), 7-11 November 2009, Cairo, Egypt [Elektronikus dokumentum]. - 16 (2009), p. 3349-3352. -
További szerzők:	Veres Péter Hajdu András (1973-) (matematikus, informatikus)
Internet cím:	Intézményi repozitóriumban (DEA) tárolt változat DOI
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