Extraction of purkinje fiber network from the image

The purkinje fiber network (PFN) is a very important conduction system in the endocardial surface of the ventricle, whose structure is crucial in ventricular physiopathologics. Traditional medical imaging methods, such as magnetic resonance imaging (MRI) or computed tomography (CT), fail to reveal the PFN information well. Another kind of method is to construct the PFN by the modeling method. i.e. modeling using the fractal structure of the endocardial surface photographs. However, among steps of the construction, PFN is extracted by the threshold value of the gray scale before the visualization, which needs the gray scale modification at some points to differentiate them from background points. In this work, we have proposed a semi-automated method for extracting PFN from the fractal structure in the left ventricle, which is proved to be feasible and efficient in the visualization and simulation of the heart, and is more adaptive to the extraction of the canine left ventricular purkinje fiber network from the fractal structure.