Abstract

This paper proposes a novel curvilinear structure detector, called Op- timally Oriented Flux (OOF). OOF finds an optimal axis on which image gradi- ents are projected in order to compute the image gradient flux. The computation of OOF is localized at the boundaries of local spherical regions. It avoids con- sidering closely located adjacent structures. The main advantage of OOF is its robustness against the disturbance induced by closely located adjacent objects. Moreover, the analytical formulation of OOF introduces no additional computa- tion load as compared to the calculation of the Hessian matrix which is widely used for curvilinear structure detection. It is experimentally demonstrated that OOF delivers accurate and stable curvilinear structure detection responses under the interference of closely located adjacent structures as well as image noise.