• DocumentCode
    777149
  • Title

    Enhancing digital cephalic radiography with mixture models and local gamma correction

  • Author

    Frosio, I. ; Ferrigno, G. ; Borghese, N.A.

  • Author_Institution
    Comput. Sci. Dept., Univ. of Milan, Italy
  • Volume
    25
  • Issue
    1
  • fYear
    2006
  • Firstpage
    113
  • Lastpage
    121
  • Abstract
    We present a new algorithm, called the soft-tissue filter, that can make both soft and bone tissue clearly visible in digital cephalic radiographies under a wide range of exposures. It uses a mixture model made up of two Gaussian distributions and one inverted lognormal distribution to analyze the image histogram. The image is clustered in three parts: background, soft tissue, and bone using this model. Improvement in the visibility of both structures is achieved through a local transformation based on gamma correction, stretching, and saturation, which is applied using different parameters for bone and soft-tissue pixels. A processing time of 1 s for 5 Mpixel images allows the filter to operate in real time. Although the default value of the filter parameters is adequate for most images, real-time operation allows adjustment to recover under- and overexposed images or to obtain the best quality subjectively. The filter was extensively clinically tested: quantitative and qualitative results are reported here.
  • Keywords
    Gaussian distribution; bone; brain; diagnostic radiography; image enhancement; medical image processing; 1 s; Gaussian distributions; bone tissue; digital cephalic radiography; image enhancement; inverted lognormal distribution; local gamma correction; mixture models; soft-tissue filter; Biological tissues; Bones; Clustering algorithms; Computer science; Diagnostic radiography; Filters; Image coding; Intelligent systems; Pixel; Visualization; Digital radiography; histogram-based clustering; image enhancement; local gamma correction; mixture models; soft-tissue filter (STF); Algorithms; Cephalometry; Computer Simulation; Connective Tissue; Head; Humans; Models, Biological; Models, Statistical; Numerical Analysis, Computer-Assisted; Radiographic Image Enhancement; Radiographic Image Interpretation, Computer-Assisted; Reproducibility of Results; Sensitivity and Specificity; Signal Processing, Computer-Assisted; Skull;
  • fLanguage
    English
  • Journal_Title
    Medical Imaging, IEEE Transactions on
  • Publisher
    ieee
  • ISSN
    0278-0062
  • Type

    jour

  • DOI
    10.1109/TMI.2005.861017
  • Filename
    1564331