Thickness dependent tortuosity estimation for retinal blood vessels.
This paper describes a framework for the automated estimation of vessel tortuosity in retinal images. We introduce a new tortuosity metric that takes into account vessel thickness, yielding estimates plausibly closer to intuition and medical judgement than those from previous metrics. We also propose an algorithm identifying automatically a vasculature segment connecting two points specified manually. Starting from a binary image of the vasculature, the algorithm computes a skeletal (medial axis) representation on which all terminal and branching points are located. This is then converted to a graph representation including connectivity as well as thickness information for all vessels. Target segments for tortuosity estimation are identified automatically from end points selected manually using a shortest-path algorithm. Results are presented and compared with those provided by clinical classification on 50 vessels from DRIVE images. An overall agreement with clinical judgement of 92.4% is achieved, superior to that of comparison measures.
Azegrouz
Hind
H
Joint Res. Inst. Signal and Image Processing, Heriot Watt Univ., Scotland, UK. ha19@hw.ac.uk
Trucco
Emanuele
E
Dhillon
Baljean
B
MacGillivray
Thomas
T
MacCormick
I J
IJ
D000465:Algorithms; D001331:Automation; D001808:Blood Vessels; D003196:Computer Graphics; D004867:Equipment Design; D006801:Humans; D007090:Image Interpretation, Computer-Assisted; D007091:Image Processing, Computer-Assisted; D021621:Imaging, Three-Dimensional; D008953:Models, Anatomic; D015233:Models, Statistical; D010363:Pattern Recognition, Automated; D012164:Retinal Diseases / Q000175:diagnosis*; D012171:Retinal Vessels / Q000473:pathology*