Hierarchical transformation-guided image registration for two-dimensional gel electrophoresis image. (January 2020)

• Record Type: Journal Article
• Authors: Wei, Lifang; Dong, Heng; Yang, Changcai; Guan, Zhe; Lin, Wenfang; Miao, Ying
• Is Part Of: Journal of algorithms & computational technology. Volume 14(2020)
• Publication Date: 2020-01
• Subjects: Gel electrophoresis image -- image registration -- L2-minimizing estimate -- hierarchical transformation-guided -- no-rigid deformation registration; Computer algorithms -- Periodicals; Numerical calculations -- Periodicals; Computer algorithms; Numerical calculations; Periodicals; 518.1
• DOI: 10.1177/1748302619895180
• Languages: English
• ISSNs: 1748-3018

Abstract

Many proteomics studies have been devoted to investigate the gel-based methods for separation and fractionation of complex protein mixture extracted from tissues, cells, and other biological specimens. In order to fractionate, identify, and quantify proteins when coupled with mass spectrometric identification or other immunological tests, accurate image alignment method for possible significant protein diversity under different experimental conditions is of high demand. Although lots of protein spot matching and alignment methods have been proposed for two-dimensional gel electrophoresis image, very few methods work well because of dynamic appearance changes and structure changes for gel electrophoresis images. To address these difficulties, we propose a hierarchical transformation-guided registration method to not only align the global shape changes by affine transformation but also alleviate the local structure changes between two gel electrophoresis images by no-rigid deformation registration. The non-rigid registration method is used to obtain the local displacement with shape changes. Since such initial affine transformation can alleviate the global transformation difference between two images, it becomes efficient to use non-rigid registration to refine the registration result. The visualized and quantified experimental results show that our method is more accurate and robust to identify the variability between individual proteins.