• DocumentCode
    534581
  • Title

    Multiscale image analysis for the quantitative evaluation of periapical lesion healings

  • Author

    Kim, Desok ; Jeong, Hyekyung ; Kim, Minjin ; Kim, Changick ; Lee, Byung-Do

  • Author_Institution
    Dept of Electr. Eng., Korea Adv. Inst. of Sci. & Technol., Daejeon, South Korea
  • Volume
    1
  • fYear
    2010
  • fDate
    16-18 Oct. 2010
  • Firstpage
    424
  • Lastpage
    427
  • Abstract
    Intraoral radiographs are used routinely for the diagnosis of periapical lesions and the evaluation of subsequent lesion treatments. However, a reliable and reproductive evaluation is often difficult due to the subjectiveness of visual interpretation and various imaging factors in routine dental radiography. To aid radiographic evaluation of periapical lesion healing, we have developed a multiscale image analysis method based on the change of bone features. Fifty pairs of pre- and post-treatment intraoral radiographs were retrospectively obtained and classified into successful (25 cases) or failed (25 cases) treatments based on the presence of periapical radiolucency. To accurately segment the bone images, an optimal scale was determined at which measured bone thickness reached 95% of the maximum values. Trabecular bone images were segmented in multiple scales to calculate intensity and skeleton features. Texture features were calculated within the user-traced lesion area. Based on relative differences of these features between pre- and post-treatment, several treatment efficacy models were derived and tested by leave one out cross validation. Intensity, skeleton, and texture features were significantly changed after successful treatment (Wilcoxon´s paired test, p<;;0.05). Treatment efficacy of periapical lesions was classified at the sensitivity of 96% and the specificity of 92% (logistic regression analysis). We found that healing lesions show not only brighter but also larger, thicker, more branched trabecular bones, while cases of persistent disease have bones with more uniform density. Our model may aid the early intervention for re-treatment of failed lesion healings.
  • Keywords
    bone; diagnostic radiography; feature extraction; image classification; image segmentation; image texture; medical image processing; pattern recognition; radiation therapy; thickness measurement; tumours; Wilcoxon´s paired test; bone features; bone thickness; dental radiography; disease; image classification; image segmentation; lesion treatments; logistic regression analysis; multiscale image analysis; pattern recognition; periapical lesion healings; periapical radiolucency; post-treatment intraoral radiographs; skeleton features; texture features; trabecular bone images; user-traced lesion area; visual interpretation; Bones; Dentistry; Image analysis; Image segmentation; Lesions; Radiography; Multiscale image analysis; Pattern recognition; Periapical lesions; Subtraction radiography;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Biomedical Engineering and Informatics (BMEI), 2010 3rd International Conference on
  • Conference_Location
    Yantai
  • Print_ISBN
    978-1-4244-6495-1
  • Type

    conf

  • DOI
    10.1109/BMEI.2010.5639554
  • Filename
    5639554