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A robust and high-performance shape registration technique using characteristic functions

A robust and high-performance shape registration technique using characteristic functions
A robust and high-performance shape registration technique using characteristic functions
We propose an innovative similarity registration method for volumetric shapes in this paper. This characteristic function-based method is intended to tackle the registration problem for the shapes containing sub-shapes in the presence of noise, and to strike a desirable balance between alignment performance and efficiency. In order to obtain the optimal parameters for scaling, rotation and translation in a reasonable time, radial moments and spherical coordinate system-based cross-correlation are exploited here. Moreover, an iterative method and principal component analysis are also employed to improve robustness of our algorithm. The shapes containing sub-shapes and the lung shapes from a CT dataset are employed in the experiments for validation. Compared with state-of-the-art algorithms, the characteristic function-based method manages to achieve excellent robustness at very low signal-to-noise ratio as well as superior registration speed, accuracy and stability in the medical shape data processing.
IEEE
Cui, Zheng
93f81116-5e97-451a-abac-fa4b43b1decf
Mahmoodi, Sasan
91ca8da4-95dc-4c1e-ac0e-f2c08d6ac7cf
Bennett, Michael
6df5585a-3d93-4870-8797-389759fc82c7
Cui, Zheng
93f81116-5e97-451a-abac-fa4b43b1decf
Mahmoodi, Sasan
91ca8da4-95dc-4c1e-ac0e-f2c08d6ac7cf
Bennett, Michael
6df5585a-3d93-4870-8797-389759fc82c7

Cui, Zheng, Mahmoodi, Sasan and Bennett, Michael (2019) A robust and high-performance shape registration technique using characteristic functions. In 2018 IEEE International Conference on Image Processing, Applications and Systems (IPAS). IEEE. 6 pp . (doi:10.1109/IPAS.2018.8708883).

Record type: Conference or Workshop Item (Paper)

Abstract

We propose an innovative similarity registration method for volumetric shapes in this paper. This characteristic function-based method is intended to tackle the registration problem for the shapes containing sub-shapes in the presence of noise, and to strike a desirable balance between alignment performance and efficiency. In order to obtain the optimal parameters for scaling, rotation and translation in a reasonable time, radial moments and spherical coordinate system-based cross-correlation are exploited here. Moreover, an iterative method and principal component analysis are also employed to improve robustness of our algorithm. The shapes containing sub-shapes and the lung shapes from a CT dataset are employed in the experiments for validation. Compared with state-of-the-art algorithms, the characteristic function-based method manages to achieve excellent robustness at very low signal-to-noise ratio as well as superior registration speed, accuracy and stability in the medical shape data processing.

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A Robust and High-performance Shape Registration Technique Using Characteristic Functions - Accepted Manuscript
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Accepted/In Press date: 2018
e-pub ahead of print date: December 2018
Published date: 9 May 2019
Venue - Dates: IEEE International Conference on Image Processing, Applications and Systems, Nice, France, 2018-12-12 - 2018-12-14

Identifiers

Local EPrints ID: 425296
URI: https://eprints.soton.ac.uk/id/eprint/425296
PURE UUID: 6a7a9ec1-2712-4e2b-9bb5-c3d6a7e99220

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Date deposited: 12 Oct 2018 16:30
Last modified: 16 Sep 2019 16:31

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Contributors

Author: Zheng Cui
Author: Sasan Mahmoodi
Author: Michael Bennett

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