NDLI: An Interpolation Method Based on Generalized Regression Neural Network for Ultrasonic 3D Reconstruction

Content Provider	IEEE Xplore Digital Library
Author	Asad Du Zhi-jiang Sun Li-ning Fereidoon, M.A. Reza, K.M.
Copyright Year	2006
Description	Author affiliation: Dept. of Mechatronics, Harbin Inst. of Technol. (Asad; Du Zhi-jiang; Sun Li-ning)
Abstract	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. We introduce a novel algorithm based on generalized regression neural network (GRNN) which can interpolate unknown volxes 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 and conserve the quality on online 3D reconstruction
Starting Page	5136
Ending Page	5140
File Size	4682469
Page Count	5
File Format	PDF
ISBN	1424402581
DOI	10.1109/IROS.2006.282607
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2006-10-09
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Interpolation Neural networks Image reconstruction Robots Shape Visualization Surgery Computed tomography Magnetic resonance imaging Ultrasonic imaging Generalized regression neural network 3D reconstruction
Content Type	Text
Resource Type	Article

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