  Detection of non-calcified plaques in the coronary tree is a challenging
problem due to the nature of comprising substances. Hard plaques are easily
discernible in CTA data cloud due to apparent bright behaviour, therefore many
approaches have been proposed for automatic segmentation of calcified plaques.
In contrast soft plaques show very small difference in intensity with respect
to surrounding heart tissues & blood voxels. This similarity in intensity makes
the isolation and detection of soft plaques very difficult. This work aims to
develop framework for segmentation of vulnerable plaques with minimal user
dependency. In first step automatic seed point has been established based on
the fact that coronary artery behaves as tubular structure through axial
slices. In the following step the behaviour of contrast agent has been modelled
mathematically to reflect the dye diffusion in respective CTA volume.
Consequently based on detected seed point & intensity behaviour, localized
active contour segmentation has been applied to extract complete coronary tree.
Bidirectional segmentation has been applied to avoid loss of coronary
information due to the seed point location whereas auto adjustment feature of
contour grabs new emerging branches. Medial axis for extracted coronary tree is
generated using fast marching method for obtaining curve planar reformation for
validation of contrast agent behaviour. Obtained coronary tree is to be
evaluated for soft plaques in second phase of this research.
