Design of Cerebrovascular Phantoms Using Fuzzy Distance Transform-based Geodesic Paths

Generation of digital phantoms specific to the patients' carotid vasculature is a complicated and challenging task because of its complex geometrical structure and interconnections. Such digital phantoms are extremely useful in quick analysis of the vascular geometry and modelling blood flows in the cerebrovasculature. All these analyses lead to effective diagnosis and detection/localization of the diseased arterial segment in the cerebrovasculature. In this work, we have proposed a semi-automatic geodesic path propagation algorithm based on fuzzy distance transform to generate digital cerebrovascular phantoms from the patients' CT angiogram (CTA) images. We have also custom-developed a 2-D/3-D user interface for accurate placement of user specified seeds on the input images. The proposed method effectively separates the artery/vein regions from the soft bones in the overlapping intensity regions using minimal human interaction. Qualitative results along with 3-D rendition of the cerebrovascular phantoms on four patients CTA images are presented here.