A Robust Mosaicing Method with Super-Resolution for Optical Medical Images

  • Mingxing Hu
  • Graeme Penney
  • Daniel Rueckert
  • Philip Edwards
  • Fernando Bello
  • Michael Figl
  • Roberto Casula
  • Yigang Cen
  • Jie Liu
  • Zhenjiang Miao
  • David Hawkes
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6326)

Abstract

Constructing a mosaicing image with a broader field-of-view has become an important topic in image guided diagnosis and treatment. In this paper, we present a robust feature-based method for video mosaicing with super-resolution for optical medical images. Firstly, outliers involved in the feature dataset are removed using trilinear constraints and iterative bundle adjustment, then a minimal cost graph path is built for mosaicing using topology inference. Finally, a mosaicing image with super-resolution is created by way of maximum a posterior (MAP) estimation and selective initialization. The proposed method has been tested with both endoscopic images from totally endoscopic coronary artery bypass surgery and fibered confocal microscopy images. The results showed our method performs better than previously reported methods in terms of accuracy and robustness to deformation and artefacts.

Keywords

Endoscopic Image Bundle Adjustment Iterative Refinement Reprojection Error Feature Dataset 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Mingxing Hu
    • 1
  • Graeme Penney
    • 2
  • Daniel Rueckert
    • 3
  • Philip Edwards
    • 4
  • Fernando Bello
    • 4
  • Michael Figl
    • 3
  • Roberto Casula
    • 5
  • Yigang Cen
    • 6
  • Jie Liu
    • 7
  • Zhenjiang Miao
    • 6
  • David Hawkes
    • 1
  1. 1.Centre for Medical Image ComputingUniversity College London 
  2. 2.Department of Imaging SciencesKing’s College London 
  3. 3.Department of ComputingImperial College 
  4. 4.Department of Surgical Oncology and TechnologyImperial College 
  5. 5.Cardiothoracic SurgerySt. Mary’s HospitalLondonUK
  6. 6.Institute of Information ScienceBeijing Jiaotong UniversityBeijingChina
  7. 7.Department of Biomedical EngineeringBeijing Jiaotong UniversityBeijingChina

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