Semi-automated extraction of canine left ventricular purkinje fiber network

The purkinje fiber network (PFN) is a very important conduction system in the endocardial surface of the ventricle, whose structure is crucial in ventricular physiopathology. Traditional medical imaging methods, such as magnetic resonance imaging (MRI) or computed tomography (CT), however, fail to reveal the detailed PFN information. An alternative is to model PFN as idealized self-similar fractal tree. Recently, a LLE-based method is proposed for the construction of the purkinje system in the canine left ventricle (LV), where curvilinear PFN structures are first detected from 2D image and then mapped to 3D surface. This method, however, adopts a simple local thresholding method to extract the curvilinear PFN structure, and thus many interactions are required to obtain the satisfactory detection result. In this work, we propose a semi-automated method for extracting both the location and the width information from the dissection image of the endocardial surface of the canine left ventricle, which is more feasible and adaptive for curvilinear PFN structure extraction.