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.
vessels mathematical morphology blood edge detection image analysis ophthalmology