• DocumentCode
    1432534
  • Title

    Contrast-Independent Curvilinear Structure Detection in Biomedical Images

  • Author

    Obara, Boguslaw ; Fricker, Mark ; Gavaghan, David ; Grau, Vicente

  • Author_Institution
    Oxford e-Res. Centre, Oxford Centre for Integrative Syst. Biol., Oxford, UK
  • Volume
    21
  • Issue
    5
  • fYear
    2012
  • fDate
    5/1/2012 12:00:00 AM
  • Firstpage
    2572
  • Lastpage
    2581
  • Abstract
    Many biomedical applications require detection of curvilinear structures in images and would benefit from automatic or semiautomatic segmentation to allow high-throughput measurements. Here, we propose a contrast-independent approach to identify curvilinear structures based on oriented phase congruency, i.e., the phase congruency tensor (PCT). We show that the proposed method is largely insensitive to intensity variations along the curve and provides successful detection within noisy regions. The performance of the PCT is evaluated by comparing it with state-of-the-art intensity-based approaches on both synthetic and real biological images.
  • Keywords
    image segmentation; medical image processing; object detection; PCT; biomedical applications; biomedical image detection; contrast-independent curvilinear structure detection; phase congruency tensor; semiautomatic segmentation; Eigenvalues and eigenfunctions; Equations; Feature extraction; Mathematical model; Noise; Tensile stress; Vectors; Bioimage informatics; curvilinear structure; live-wire tracing; phase congruency tensor (PCT); Algorithms; Artificial Intelligence; Image Enhancement; Image Interpretation, Computer-Assisted; Pattern Recognition, Automated; Reproducibility of Results; Sensitivity and Specificity;
  • fLanguage
    English
  • Journal_Title
    Image Processing, IEEE Transactions on
  • Publisher
    ieee
  • ISSN
    1057-7149
  • Type

    jour

  • DOI
    10.1109/TIP.2012.2185938
  • Filename
    6140570