2D fast vessel visualization using a vessel wall mask guiding fine vessel detection

Raptis, S; Koutsouris, D

dc.contributor.author	Raptis, S	en
dc.contributor.author	Koutsouris, D	en
dc.date.accessioned	2014-03-01T01:32:25Z
dc.date.available	2014-03-01T01:32:25Z
dc.date.issued	2010	en
dc.identifier.issn	16874188	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/20119
dc.subject.other	Centerlines	en
dc.subject.other	Diagnostic applications	en
dc.subject.other	Gaussians	en
dc.subject.other	Gradient filters	en
dc.subject.other	Grey levels	en
dc.subject.other	Initial estimate	en
dc.subject.other	Initial segmentation	en
dc.subject.other	Intensity variations	en
dc.subject.other	Optimal parameter	en
dc.subject.other	Region growing	en
dc.subject.other	ROC analysis	en
dc.subject.other	Spatial arrangements	en
dc.subject.other	Spatial filters	en
dc.subject.other	Vasculature	en
dc.subject.other	Vessel detection	en
dc.subject.other	Vessel segments	en
dc.subject.other	Vessel visualization	en
dc.subject.other	Vessel walls	en
dc.subject.other	Visual feature	en
dc.subject.other	Diagnosis	en
dc.subject.other	Hough transforms	en
dc.subject.other	Matched filters	en
dc.subject.other	Visualization	en
dc.subject.other	Pixels	en
dc.title	2D fast vessel visualization using a vessel wall mask guiding fine vessel detection	en
heal.type	journalArticle	en
heal.identifier.primary	10.1155/2010/580518	en
heal.identifier.secondary	580518	en
heal.identifier.secondary	http://dx.doi.org/10.1155/2010/580518	en
heal.publicationDate	2010	en
heal.abstract	The paper addresses the fine retinal-vessel's detection issue that is faced in diagnostic applications and aims at assisting in better recognizing fine vessel anomalies in 2D. Our innovation relies in separating key visual features vessels exhibit in order to make the diagnosis of eventual retinopathologies easier to detect. This allows focusing on vessel segments which present fine changes detectable at different sampling scales. We advocate that these changes can be addressed as subsequent stages of the same vessel detection procedure. We first carry out an initial estimate of the basic vessel-wall's network, define the main wall-body, and then try to approach the ridges and branches of the vasculature's using fine detection. Fine vessel screening looks into local structural inconsistencies in vessels properties, into noise, or into not expected intensity variations observed inside pre-known vessel-body areas. The vessels are first modelled sufficiently but not precisely by their walls with a tubular model-structure that is the result of an initial segmentation. This provides a chart of likely Vessel Wall Pixels (VWPs) yielding a form of a likelihood vessel map mainly based on gradient filter's intensity and spatial arrangement parameters (e.g., linear consistency). Specific vessel parameters (centerline, width, location, fall-away rate, main orientation) are post-computed by convolving the image with a set of pre-tuned spatial filters called Matched Filters (MFs). These are easily computed as Gaussian-like 2D forms that use a limited range sub-optimal parameters adjusted to the dominant vessel characteristics obtained by Spatial Grey Level Difference statistics limiting the range of search into vessel widths of 16, 32, and 64 pixels. Sparse pixels are effectively eliminated by applying a limited range Hough Transform (HT) or region growing. Major benefits are limiting the range of parameters, reducing the search-space for post-convolution to only masked regions, representing almost 2 of the 2D volume, good speed versus accuracy/time trade-off. Results show the potentials of our approach in terms of time for detection ROC analysis and accuracy of vessel pixel (VP) detection. Copyright © 2010 Sotirios Raptis and Dimitris Koutsouris.	en
heal.journalName	International Journal of Biomedical Imaging	en
dc.identifier.doi	10.1155/2010/580518	en
dc.identifier.volume	2010	en