An interpolation method based on generalized regression neural network for ultrasonic 3D reconstruction.

被引：1

作者：

Asad, Babakhani ^{[1
]}

Du Zhi-Jiang ^{[1
]}

Sun Li-ning ^{[1
]}

Fereidoon, Mianji Abdollah ^{[2
]}

Reza, Kardan Mohammad ^{[2
]}

机构：

[1] Harbin Inst Technol, Mechatron Dept, Harbin 150006, Peoples R China

[2] Natl Regulatory Author Org, Radiat Protect Dept, Tehran, Iran

来源：

2006 IEEE/RSJ INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS, VOLS 1-12 | 2006年

关键词：

generalized regression neural network; 3D reconstruction; visualization;

D O I：

10.1109/IROS.2006.282607

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

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 We introduce a novel algorithm 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 and conserve the quality on online 3D reconstruction.

引用

页码：5136 / +

页数：2

共 16 条

[1]

[Anonymous], 10 NORD C BIOM 1 S1

[2]

GRAHAM T, 2001, MED IMAGE ANAL, V5, P41

[3] An objective comparison of 3-D image interpolation methods [J].

Grevera, GJ ;

Udupa, JK .

IEEE TRANSACTIONS ON MEDICAL IMAGING, 1998, 17 (04) :642-652

[4]

GUA JF, 1995, COMPUTERIZED MED IMA, V19, P267

[5] SHAPE-BASED INTERPOLATION [J].

HERMAN, GT ;

ZHENG, JS ;

BUCHOLTZ, CA .

IEEE COMPUTER GRAPHICS AND APPLICATIONS, 1992, 12 (03) :69-79

[6] Heuristic pattern correction scheme using adaptively trained generalized regression neural networks [J].

Hoya, T ;

Chambers, JA .

IEEE TRANSACTIONS ON NEURAL NETWORKS, 2001, 12 (01) :91-100

[7] Neural network prediction of obstructive sleep apnea from clinical criteria [J].

Kirby, SD ;

Danter, W ;

George, CFP ;

Francovic, T ;

Ruby, RRF ;

Ferguson, KA .

CHEST, 1999, 116 (02) :409-415

[8]

Lohmann G., 1998, Volumetric image analysis

[9] Radial basis function networks in host-guest interactions: instant and accurate formation constant calculations [J].

Loukas, YL .

ANALYTICA CHIMICA ACTA, 2000, 417 (02) :221-229

[10] A neural classifier enabling high-throughput topological analysis of lymphocytes in tissue sections [J].

Nattkemper, TW ;

Ritter, HJ ;

Schubert, W .

IEEE TRANSACTIONS ON INFORMATION TECHNOLOGY IN BIOMEDICINE, 2001, 5 (02) :138-149

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