形态膨胀的3D区域生长气管分割算法  

3D Region Growing Algorithm Driven by Morphological Dilation for Airway Tree Segmentation in Image Guided Therapy

作　　者：王雷[1,2] 高欣[1] 张桂芝[3] 

机构地区：[1]中国科学院苏州生物医学工程技术研究所,苏州215163 [2]中国科学院研究生院,北京100049 [3]中国人民解放军总医院南楼呼吸科,北京100853

出　　处：《生物医学工程学杂志》2013年第4期679-683,691,共6页Journal of Biomedical Engineering

基　　金：国家自然科学基金资助项目(81000651);江苏省自然科学基金资助项目(BK2010236)

摘　　要：在影像引导介入治疗中,对肺部区域的气管进行准确分割与提取不仅有助于辨别气管解剖细节,而且能避免治疗中对气管造成较大的损伤。为此,本文提出一种新算法,首先利用区域生长法对气管树粗分割,再利用形态膨胀法和区域生长法扩大气管区域,对其提取中心线,以中心线为基础进行细分割。将粗分割和细分割的结果取或作为最终气管树。将本算法应用到6例临床CT数据中,实验结果显示本算法至少能分割出6阶,最多达9阶的细支气管,6阶支气管分割精确度平均达到63.5%。表明本文算法基本满足影像引导介入治疗对气管树分割的要求。The precise three-dimensional （3-D） segmentation of airway from CT image is essential for the imageguided therapy, which helps avoid a serious airway injury. We proposed a new segmentation algorithm for the calculation. Firstly, region growing method was employed to segment the main bronchioles （rough segmentation）. And then region growing driven by morphology dilation was used to expand the airway region, where centerline of airway tree was extracted. Terminal bronchia （fine segmentation） were segmented along the centerline. Ultimately, rough and fine segmentation results are combined by a logical OR as final airway tree. Quantitative comparisons with 6 sets of manual segmentation results showed that the algorithm could be used to segment up to 9 bronchia, and the average branch sensitivity of 6^th was 63.5%, meeting the requirement of airway tree in the image-guided therapy.

关 键 词：影像引导介入 气管树分割 区域生长法 CT体数据 形态学膨胀 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]