Segmentation of vessel-like patterns using mathematical morphology and curvature evaluation

This paper presents an algorithm based on mathematical morphology and curvature evaluation for the detection of vessel-like patterns in a noisy environment. Such patterns are very common in medical images. Vessel detection is interesting for the computation of parameters related to blood flow, Its tree-like geometry makes it a usable feature for registration between images that can be of a different nature. In order to define vessel-like patterns, segmentation will be performed with respect to a precise model. We define a vessel as a bright pattern, piece-wise connected, and locally linear. Mathematical Morphology is very well adapted to this description, however other patterns fit such a morphological description, In order to differentiate vessels from analogous background patterns, a cross-curvature evaluation is performed. They are separated out as they have a specific Gaussian-like profile whose curvature varies smoothly along the vessel. The detection algorithm that derives directly from this modeling is based on four steps: 1) noise reduction; 2) linear pattern with Gaussian-like profile improvement; 3) cross-curvature evaluation; 4) linear filtering. We present its theoretical background and illustrate it on real images of various natures, then evaluate its robustness and its accuracy with respect to noise.