Application of generalized regression neural network on fast 3D reconstruction

被引：0

作者：

Babakhani Asad ^{[1
]}

杜志江 ^{[1
]}

孙立宁 ^{[1
]}

Kardan Reza ^{[2
]}

Mianji A Fereidoun ^{[2
]}

机构：

[1] Robotics Institute, Harbin Institute of Technology

[2] Radiation Protection Department, National Regulatory Authority Organization of Iran

来源：

JournalofHarbinInstituteofTechnology | 2007年 / 01期

关键词：

generalized regression neural network; 3D reconstruction; visualization;

D O I：

暂无

中图分类号：

TP183 [人工神经网络与计算]; TP391.41 [];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ; 080203 ;

摘要：

In robot-assisted surgery projects,researchers should be able to make fast 3D reconstruction. Usually 2D images acquired with common diagnostic equipments such as UT, CT and MRI are not enough and complete for an accurate 3D reconstruction. There are some interpolation methods for approximating non value voxels which consume large execution time. A novel algorithm is introduced based on generalized regression neural network (GRNN) which can interpolate unknown voxles fast and reliable. The GRNN interpolation is used to produce new 2D images between each two succeeding ultrasonic images. It is shown that the composition of GRNN with image distance transformation can produce higher quality 3D shapes. The results of this method are compared with other interpolation methods practically. It shows this method can decrease overall time consumption on online 3D reconstruction.

引用

页码：9 / 12

页数：4