title: The plant virus microscope image registration method based on mismatches removing.
abstract: The electron microscopy is one of the major means to observe the virus. The view of virus microscope images is limited by making specimen and the size of the camera's view field. To solve this problem, the virus sample is produced into multi-slice for information fusion and image registration techniques are applied to obtain large field and whole sections. Image registration techniques have been developed in the past decades for increasing the camera's field of view. Nevertheless, these approaches typically work in batch mode and rely on motorized microscopes. Alternatively, the methods are conceived just to provide visually pleasant registration for high overlap ratio image sequence. This work presents a method for virus microscope image registration acquired with detailed visual information and subpixel accuracy, even when overlap ratio of image sequence is 10% or less. The method proposed focus on the correspondence set and interimage transformation. A mismatch removal strategy is proposed by the spatial consistency and the components of keypoint to enrich the correspondence set. And the translation model parameter as well as tonal inhomogeneities is corrected by the hierarchical estimation and model select. In the experiments performed, we tested different registration approaches and virus images, confirming that the translation model is not always stationary, despite the fact that the images of the sample come from the same sequence. The mismatch removal strategy makes building registration of virus microscope images at subpixel accuracy easier and optional parameters for building registration according to the hierarchical estimation and model select strategies make the proposed method high precision and reliable for low overlap ratio image sequence.
authors.affiliation: College of Computer and Information Science, Fujian Agriculture and Forestry University, Fuzhou 350002, China. Electronic address: weilifang1981@163.com.
authors.forename: Lifang
authors.initials: L
authors.lastname: Wei
authors.affiliation: College of Computer and Information Science, Fujian Agriculture and Forestry University, Fuzhou 350002, China. Electronic address: zhoushuch@sohu.com.
authors.forename: Shucheng
authors.initials: S
authors.lastname: Zhou
authors.affiliation: College of Physics and Information Engineering, FuZhou University, Fuzhou 350002, China. Electronic address: ypoofml@163.com.
authors.forename: Heng
authors.initials: H
authors.lastname: Dong
authors.affiliation: Fujian Province Key Laboratory of Plant Virology, Institute of Plant Virology, Fujian Agriculture and Forestry University, Fuzhou 350002, China. Electronic address: hermione9@163.com.
authors.forename: Qianzhuo
authors.initials: Q
authors.lastname: Mao
authors.affiliation: College of Computer and Information Science, Fujian Agriculture and Forestry University, Fuzhou 350002, China. Electronic address: linjx2000@gmail.com.
authors.forename: Jiaxiang
authors.initials: J
authors.lastname: Lin
authors.affiliation: College of Computer and Information Science, Fujian Agriculture and Forestry University, Fuzhou 350002, China. Electronic address: riqing.chen@fafu.edu.cn.
authors.forename: Riqing
authors.initials: R
authors.lastname: Chen
mesh_terms: D007091:Image Processing, Computer-Assisted / Q000379:methods*; D046529:Microscopy, Electron, Transmission / Q000379:methods*; D012275:Oryza / Q000821:virology*; D010942:Plant Viruses / Q000648:ultrastructure*; D012087:Reoviridae / Q000648:ultrastructure*
keywords: Image registration; Mismatching removal; Transformation models; Virus microscope image