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Blood Vessel Segmentation in Angiograms using Fuzzy Inference System and Mathematical Morphology

Segmentation of blood vessels is one of the essential medical computing tools for clinical assessment of vascular diseases. It is a process of partitioning an angiogram into non overlapping vascular and background regions. Based on the partitioning results, surfaces of vasculatures can be extracted, modeled, manipulated, measured and visualized. Edge detection is an essential task in computer vision. It covers a wide range of application, from segmentation to pattern matching. Angiography is a widely used procedure for vessel observation in both clinical routine and medical research. Often for the subsequent analysis of the vasculature it is needed to measure the angiogram area covered by vessels and/or the vessel length. For this purpose we need vessel enhancement and segmentation. In this paper, we evaluate the performance of a fuzzy inference system and morphology filters for blood vessel segmentation in a noise angiograms image.