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Extra info for Advanced Concepts for Intelligent Vision Systems: 17th International Conference, ACIVS 2016, Lecce, Italy, October 24-27, 2016, Proceedings
These segmentation contours have the same number of points, making it possible to deﬁne edges between slices to generate a face-vertex mesh. The deformable modelling process is iterative and stops when the maximum number of iterations is reached. This process consists of two components. Firstly a 2D boundary detector is carried out on each individual slice, where search paths are deﬁned along the normal direction of each mesh vertex. A neural network is used for boundary detection in order to learn useful features, rather than spending time hand crafting them.
The pairwise symmetry coeﬃcient f (p, q) is deﬁned as : ⊥ )τp | f (p, q) = |τq S(Tpq (9) ⊥ ) is the reﬂection matrix with respect to the line perpendicular to (pq): where S(Tpq ⊥ S(Tpq )= − cos(2θpq ) − sin(2θpq ) − sin(2θpq ) cos(2θpq ) (10) In addition to the geometric edge information, the symmetry degree of the two regions around p and q can be measured by comparing their corresponding local orientation histogram hp and hq . Due to symmetry considerations, one of the two histograms must be shifted and reversed before comparison.
Boundary detection performed well, with boundary sensitivity of >88 % and speciﬁcity of >85 % for highly obscured and diﬀused lymphatic vessel walls. In addition, the vast majority of all boundary-classiﬁed pixels were in the immediate vicinity of the ground truth boundary. When integrated into a 3D deformable modelling framework it produced an area overlap with the ground truth of >98 %, and both point-to-mesh and Hausdorﬀ distance errors were less than other approaches. To this end it has been shown that NNs are suitable for boundary detection in deformable modelling, where object boundaries are obscured, diﬀused and low in contrast.