Extraction of the skeleton of vascular structures is an important procedure for computer aided analysis of vascular data. A new automatic skeletonization algorithm for 3D vascular volumes is proposed. Two types of distance maps and clusters, a set of connected points with the same property are used to represent the vascular structure. Using clusters representation, branch information can be retrieved efficiently. In each identified branch, preliminary points, defined as skeleton nodes, are derived hierarchically which are later interpolated to generate the skeleton. The algorithm was tested on MR angiography arterial and venous 3D vascular volumes. The extracted skeletons were reliable representation of the vascular structure. Compared to other 3D distance-based skeletonization algorithms, the new approach yields a more centered skeleton without complex post-processing. The skeleton is also insensitive to boundary complexity and can be easily modified by the user.
Abstract. In vehicle communications, channel characteristic experiences time and frequency selective fading due to high velocity of vehicle and rapid changes of surrounding scatters. The packet format for IEEE 802.11p standard limits the choice of channel estimation algorithms. Conventional channel estimation algorithms perform the channel estimation based on the long preamble training sequence, then applies the estimated channel response to compensate for the entire packet. These algorithms are not optimal for a doubly selective channel in vehicle communications. In this paper, to overcome the effect of doubly selective channel, we propose a novel pilot insertion scheme that covers all subcarriers in both the time and frequency domains simultaneously. Adaptive channel estimation and equalization algorithms are then developed based on the new system architecture. Simulations show significant improvements comparing to other exiting methods.
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