Robust Adaptive Principal Component Analysis Based on Intergraph Matrix for Medical Image Registration

Joint Authors

Leng, Chengcai
Xiao, Jinjun
Li, Min
Zhang, Haipeng

Source

Computational Intelligence and Neuroscience

Issue

Vol. 2015, Issue 2015 (31 Dec. 2015), pp.1-7, 7 p.

Publisher

Hindawi Publishing Corporation

Publication Date

2015-04-19

Country of Publication

Egypt

No. of Pages

7

Main Subjects

Biology

Abstract EN

This paper proposes a novel robust adaptive principal component analysis (RAPCA) method based on intergraph matrix for image registration in order to improve robustness and real-time performance.

The contributions can be divided into three parts.

Firstly, a novel RAPCA method is developed to capture the common structure patterns based on intergraph matrix of the objects.

Secondly, the robust similarity measure is proposed based on adaptive principal component.

Finally, the robust registration algorithm is derived based on the RAPCA.

The experimental results show that the proposed method is very effective in capturing the common structure patterns for image registration on real-world images.

American Psychological Association (APA)

Leng, Chengcai& Xiao, Jinjun& Li, Min& Zhang, Haipeng. 2015. Robust Adaptive Principal Component Analysis Based on Intergraph Matrix for Medical Image Registration. Computational Intelligence and Neuroscience،Vol. 2015, no. 2015, pp.1-7.
https://search.emarefa.net/detail/BIM-1057766

Modern Language Association (MLA)

Leng, Chengcai…[et al.]. Robust Adaptive Principal Component Analysis Based on Intergraph Matrix for Medical Image Registration. Computational Intelligence and Neuroscience No. 2015 (2015), pp.1-7.
https://search.emarefa.net/detail/BIM-1057766

American Medical Association (AMA)

Leng, Chengcai& Xiao, Jinjun& Li, Min& Zhang, Haipeng. Robust Adaptive Principal Component Analysis Based on Intergraph Matrix for Medical Image Registration. Computational Intelligence and Neuroscience. 2015. Vol. 2015, no. 2015, pp.1-7.
https://search.emarefa.net/detail/BIM-1057766

Data Type

Journal Articles

Language

English

Notes

Includes bibliographical references

Record ID

BIM-1057766