Directional histogram ratio at random probes: A local thresholding criterion for capillary images

  • Authors:

  • Na Lu;Jharon Silva;Yu Gu;Scott Gerber;Hulin Wu;Harris Gelbard;Stephen Dewhurst;Hongyu Miao

  • Affiliations:

  • State Key Laboratory for Manufacturing Systems Engineering, Systems Engineering Institute, Xi'an Jiaotong University, Xi'an Shaanxi, China and Department of Biostatistics and Computational Biology ...;Department of Microbiology and Immunology, University of Rochester, Rochester, NY, USA;Department of Biostatistics and Computational Biology, University of Rochester, Rochester, NY 14642, USA;Department of Microbiology and Immunology, University of Rochester, Rochester, NY, USA;Department of Biostatistics and Computational Biology, University of Rochester, Rochester, NY 14642, USA;Department of Neurology, University of Rochester, Rochester, NY, USA;Department of Microbiology and Immunology, University of Rochester, Rochester, NY, USA;Department of Biostatistics and Computational Biology, University of Rochester, Rochester, NY 14642, USA

  • Venue:
  • Pattern Recognition
  • Year:
  • 2013

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Abstract

With the development of micron-scale imaging techniques, capillaries can be conveniently visualized using methods such as two-photon and whole mount microscopy. However, the presence of background staining, leaky vessels and the diffusion of small fluorescent molecules can lead to significant complexity in image analysis and loss of information necessary to accurately quantify vascular metrics. One solution to this problem is the development of accurate thresholding algorithms that reliably distinguish blood vessels from surrounding tissue. Although various thresholding algorithms have been proposed, our results suggest that without appropriate pre- or post-processing, the existing approaches may fail to obtain satisfactory results for capillary images that include areas of contamination. In this study, we propose a novel local thresholding algorithm, called directional histogram ratio at random probes (DHR-RP). This method explicitly considers the geometric features of tube-like objects in conducting image binarization, and has a reliable performance in distinguishing small vessels from either clean or contaminated background. Experimental and simulation studies suggest that our DHR-RP algorithm is superior over existing thresholding methods.