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2D Fast Vessel Visualization Using a Vessel Wall Mask Guiding Fine Vessel Detection
Joint Authors
Raptis, Sotirios
Koutsouris, Dimitris
Source
International Journal of Biomedical Imaging
Issue
Vol. 2010, Issue 2010 (31 Dec. 2010), pp.1-20, 20 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2010-07-29
Country of Publication
Egypt
No. of Pages
20
Main Subjects
Abstract EN
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.
American Psychological Association (APA)
Raptis, Sotirios& Koutsouris, Dimitris. 2010. 2D Fast Vessel Visualization Using a Vessel Wall Mask Guiding Fine Vessel Detection. International Journal of Biomedical Imaging،Vol. 2010, no. 2010, pp.1-20.
https://search.emarefa.net/detail/BIM-482406
Modern Language Association (MLA)
Raptis, Sotirios& Koutsouris, Dimitris. 2D Fast Vessel Visualization Using a Vessel Wall Mask Guiding Fine Vessel Detection. International Journal of Biomedical Imaging No. 2010 (2010), pp.1-20.
https://search.emarefa.net/detail/BIM-482406
American Medical Association (AMA)
Raptis, Sotirios& Koutsouris, Dimitris. 2D Fast Vessel Visualization Using a Vessel Wall Mask Guiding Fine Vessel Detection. International Journal of Biomedical Imaging. 2010. Vol. 2010, no. 2010, pp.1-20.
https://search.emarefa.net/detail/BIM-482406
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-482406