• DocumentCode
    2135217
  • Title

    Ribs segmentation based on image fusion and wavelet de-noising

  • Author

    Wencheng Cui ; Tianshu Yu ; Lijie Ren ; Hong Shao

  • Author_Institution
    Sch. of Inf. Sci. & Eng., Shenyang Univ. of Technol., Shenyang, China
  • fYear
    2012
  • fDate
    16-18 Oct. 2012
  • Firstpage
    362
  • Lastpage
    366
  • Abstract
    With the development of image processing technology, computer-aided diagnosis technology has been widely used in digital chest radiographs lesions detection. As the special nature of medical image, the complexity of image information, and especially the interference of lung vessels and bone cause great difficulties in the texture analysis of lung parenchyma. A new ribs segmentation method is proposed based on the characteristics of medical image. Firstly, the complete region of lung parenchyma was extracted by Otsu after implementation of Gaussian mask and wavelet image fusion algorithm. Secondly, multi-scale wavelet was applied to reduce the noise of the lung fields. Thirdly, ribs were segmented by Gaussian mask. Finally, the edge of the ribs was smoothed. The experiment results show that the algorithm can guarantee to maintain the information of the lungs and ribs.
  • Keywords
    Gaussian processes; diagnostic radiography; image denoising; image fusion; image segmentation; lung; medical image processing; wavelet transforms; Gaussian mask; bone interference; computer-aided diagnosis technology; digital chest radiographs lesions detection; image fusion; image processing technology; lung parenchyma; lung vessel interference; noise reduction; ribs segmentation; texture analysis; wavelet denoising; Digital chest radiographs; Gaussian mask method; image fusion; lung enhancement; wavelet de-noising;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Biomedical Engineering and Informatics (BMEI), 2012 5th International Conference on
  • Conference_Location
    Chongqing
  • Print_ISBN
    978-1-4673-1183-0
  • Type

    conf

  • DOI
    10.1109/BMEI.2012.6513063
  • Filename
    6513063