CT体数据中心环绕特征检测算法及其CUDA加速

针对CT体数据的多尺度特征点检测计算量大、耗时长的问题,提出一种三维中心环绕特征快速检测算法。设计三维中心环绕特征检测子,结合三维积分图像快速生成图像的尺度空间,同时利用三维Harris边缘判定准则去除边缘点,增强特征点的稳定性。实验结果表明,相比于经典的三维Do G和SURF检测子,算法计算时间显著降低(检测时间约为三维Do G检测子的1/8,三维SURF检测子的1/2),同时相比于三维SURF检测子,特征点检测重复率也有一定程度的提高。最后,对三维中心环绕特征检测算法进行并行性分析,并分别从尺度空间生成和特征点搜索及边缘抑制两部分进行CUDA并行加速。实验结果表明,经CUDA加速后,算法能得到10倍左右的加速比,特征点检测过程耗时基本达到实际应用需求。

Visualization of Three-dimensional Human Data Based on CT Image

Three-dimensional medical image visualization becomes an essential part for medical field, including computer aided diagnosis, surgery planning and simulation, artificial limb surgery, radiotherapy planning, and teaching etc. In this paper, marching cubes algorithm is adopted to reconstruct the 3-D images for the CT image sequence in DICOM format under theVC＋＋6.0 and the visual package VTK platform. The relatively simple interactive operations such as rotation and transfer can be realized on the platform. Moreover, the normal vector and interior point are calculated to form the virtual clipping plane, which is then used to incise the 3-D object. Information of the virtual slice can be obtained, in the mean while the virtual slice images are displayed on the screen. The technique can realize the real time interaction extraction of virtual slice on 3-D CT image. The cuboids structured can be zoomed, moved and eircumrotated by operating mouse to incise the 3-D reconstruction object. Real time interaction can be realized by clipping the reconstruction object. The coordinates can be acquired by the mouse clicking in the 3D space, to realize the point mouse pick-up as well angle and distance interactive measurement. We can get quantitative information about 3-D images through measurement.

形态膨胀的3D区域生长气管分割算法

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。