A morphological image processing method for locating myosin filaments in muscle electron micrographs

  • Authors:

  • B. Bödvarsson;S. Klim;M. Mørkebjerg;S. Mortensen;C. H. Yoon;J. Chen;J. R. Maclaren;P. K. Luther;J. M. Squire;P. J. Bones;R. P. Millane

  • Affiliations:

  • Computational Imaging Group, Department of Electrical and Computer Engineering, University of Canterbury, Private Bag 4800, Christchurch, New Zealand;Computational Imaging Group, Department of Electrical and Computer Engineering, University of Canterbury, Private Bag 4800, Christchurch, New Zealand;Computational Imaging Group, Department of Electrical and Computer Engineering, University of Canterbury, Private Bag 4800, Christchurch, New Zealand;Computational Imaging Group, Department of Electrical and Computer Engineering, University of Canterbury, Private Bag 4800, Christchurch, New Zealand;Computational Imaging Group, Department of Electrical and Computer Engineering, University of Canterbury, Private Bag 4800, Christchurch, New Zealand;Computational Imaging Group, Department of Electrical and Computer Engineering, University of Canterbury, Private Bag 4800, Christchurch, New Zealand;Computational Imaging Group, Department of Electrical and Computer Engineering, University of Canterbury, Private Bag 4800, Christchurch, New Zealand;National Heart and Lung Institute, Sir Alexander Fleming Building, Imperial College London, London SW7 2AZ, UK;Muscle Contraction Group, Department of Physiology, University of Bristol, Bristol BS8 1TD, UK;Computational Imaging Group, Department of Electrical and Computer Engineering, University of Canterbury, Private Bag 4800, Christchurch, New Zealand;Computational Imaging Group, Department of Electrical and Computer Engineering, University of Canterbury, Private Bag 4800, Christchurch, New Zealand

  • Venue:
  • Image and Vision Computing
  • Year:
  • 2008

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Abstract

A morphological image processing based method for determining the positions of myosin filaments in electron micrographs of muscle cross-sections is described. The filaments are embedded in a noisy and variable background in the images, but lie on a relatively regular lattice. The filament positions are determined by a novel implementation of morphological grayscale reconstruction in which a threshold is optimised by using the lattice regularity of the derived filament markers. This approach leads to a robust algorithm. The method is applied to a number of muscle micrographs.