Banerjee Pat, Hu Mengqi, Kannan Rahul, Krishnaswamy Srinivasan
Department of Mechanical and Industrial Engineering, MC 251, 2039 ERF, 842 W. Taylor St., Chicago, IL, 60607, USA.
J Digit Imaging. 2017 Aug;30(4):519-527. doi: 10.1007/s10278-017-9985-2.
The Sensimmer platform represents our ongoing research on simultaneous haptics and graphics rendering of 3D models. For simulation of medical and surgical procedures using Sensimmer, 3D models must be obtained from medical imaging data, such as magnetic resonance imaging (MRI) or computed tomography (CT). Image segmentation techniques are used to determine the anatomies of interest from the images. 3D models are obtained from segmentation and their triangle reduction is required for graphics and haptics rendering. This paper focuses on creating 3D models by automating the segmentation of CT images based on the pixel contrast for integrating the interface between Sensimmer and medical imaging devices, using the volumetric approach, Hough transform method, and manual centering method. Hence, automating the process has reduced the segmentation time by 56.35% while maintaining the same accuracy of the output at ±2 voxels.
Sensimmer平台代表了我们在3D模型的触觉与图形同步渲染方面正在进行的研究。为了使用Sensimmer模拟医学和外科手术过程，必须从医学成像数据（如磁共振成像（MRI）或计算机断层扫描（CT））中获取3D模型。图像分割技术用于从图像中确定感兴趣的解剖结构。3D模型通过分割获得，并且为了进行图形和触觉渲染需要对其进行三角简化。本文重点介绍基于像素对比度自动分割CT图像以创建3D模型，从而整合Sensimmer与医学成像设备之间的接口，采用体积法、霍夫变换法和手动定心方法。因此，自动化该过程在保持输出精度在±2体素不变的同时，将分割时间减少了56.35%。
