Hao J T, Li M L, Tang F L
Department of Computer Science and Engineering Shanghai, Jiaotong University, Min Hang, Shanghai, People's Republic of China.
Med Biol Eng Comput. 2008 Jan;46(1):75-83. doi: 10.1007/s11517-007-0244-4. Epub 2007 Sep 6.
Accurate segmentation of the human vasculature is an important prerequisite for a number of clinical procedures, such as diagnosis, image-guided neurosurgery and pre-surgical planning. In this paper, an improved statistical approach to extracting whole cerebrovascular tree in time-of-flight magnetic resonance angiography is proposed. Firstly, in order to get a more accurate segmentation result, a localized observation model is proposed instead of defining the observation model over the entire dataset. Secondly, for the binary segmentation, an improved Iterative Conditional Model (ICM) algorithm is presented to accelerate the segmentation process. The experimental results showed that the proposed algorithm can obtain more satisfactory segmentation results and save more processing time than conventional approaches, simultaneously.
准确分割人体血管系统是许多临床程序的重要前提，如诊断、图像引导神经外科手术和术前规划。本文提出了一种改进的统计方法，用于在时间飞跃磁共振血管造影中提取整个脑血管树。首先，为了获得更准确的分割结果，提出了一种局部观察模型，而不是在整个数据集上定义观察模型。其次，对于二值分割，提出了一种改进的迭代条件模型（ICM）算法来加速分割过程。实验结果表明，与传统方法相比，该算法能够同时获得更满意的分割结果并节省更多处理时间。
