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

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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


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