An Active Testing Model for Tracking Roads in Satellite Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Mean Shift: A Robust Approach Toward Feature Space Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
A review of vessel extraction techniques and algorithms
ACM Computing Surveys (CSUR)
A unified minimal path tracking and topology characterization approach for vascular analysis
MIAR'10 Proceedings of the 5th international conference on Medical imaging and augmented reality
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The inclusion of the free-running Purkinje network in computational simulations provides a significant insight into understanding the mechanisms of cardiac pathophysiologies. However, its automatic extraction is challenging due to the presence of abundant local complexities. We thereby introduce a novel algorithm to track the Purkinje fibers in high resolution magnetic resonance (MR) images. Our formulation successively identifies local fiber orientations by using a nonlinear oriented filter. Specifically, the filter is used to compute several local profiles, from which one can estimate the orientation distribution function (ODF). The algorithm then determines the directions to be followed by detecting the modes of the local ODF using different spherical clustering methods. We quantitatively compare the accuracy of the tracked fibers with manually delineated anatomical structures.