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International Journal of Biomedical ImagingVolume 2010 2010, Article ID 580518, 20 pages

Research ArticleBiomedical Engineering Laboratory, School of Electrical and Computer Engineering, National Technical University of Athens, 9 Iroon Polytechniou Str., H-Y Building-Zografou Campus, 15773 Athens, Greece

Received 1 October 2009; Revised 8 February 2010; Accepted 12 April 2010

Academic Editor: Guo W. Wei

Copyright © 2010 Sotirios Raptis and Dimitris Koutsouris. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


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 finechanges detectable at different sampling scales. We advocate that these changes can be addressed as subsequent stages of the samevessel 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.

Author: Sotirios Raptis and Dimitris Koutsouris

Source: https://www.hindawi.com/


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