Robust Adaptive Principal Component Analysis Based on Intergraph Matrix for Medical Image Registration. (19th April 2015)

• Record Type: Journal Article
• Authors: Leng, Chengcai; Xiao, Jinjun; Li, Min; Zhang, Haipeng
• Other Names: Camastra Francesco Academic Editor.
• Is Part Of: Computational intelligence and neuroscience. Volume 2015(2015)
• Publication Date: 2015-04-19
• Subjects: Neurosciences -- Data processing -- Periodicals; Computational intelligence -- Periodicals; Computational neuroscience -- Periodicals; 612.80285
• DOI: 10.1155/2015/829528
• Languages: English
• ISSNs: 1687-5265

Abstract

Abstract : 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.