• DocumentCode
    2115723
  • Title

    Application in Stomach Epidermis Tumors Segmentation by GVF Snake Model

  • Author

    Hongbin, Zhang ; Guangli, Li

  • Author_Institution
    Sch. of Inf. Eng., East China Jiaotong Univ., Nanchang
  • fYear
    2008
  • fDate
    18-18 Dec. 2008
  • Firstpage
    453
  • Lastpage
    456
  • Abstract
    GVF snake model means gradient vector flow snake model. It is a representative of the active contour model and it has been used to solve image segmentation for some conglutinated images. GVF snake model is also a new accurate arithmetic to obtain the boundary of the object edge especially for those stomach epidermis tumors which have several conglutinated areas. As the first step of this arithmetic, many pretreatment processes are necessary such as the gauss blur and the edge mapping to get the clearer image boundary before starting the image segmentation. The next step after pretreatment processes is calculating the GVF outside force field which makes the snake move quicker to the object boundary after the initial points been selected. The experiments also show that as the key parameter of GVF snake model, mu must be set below 0.3 in order to get better segmentation results, otherwise the snake cannot move closer to the object boundary because the GVF outside force field dies down.
  • Keywords
    edge detection; gradient methods; image restoration; image segmentation; medical image processing; tumours; GVF snake model; Gauss blur; active contour model; edge mapping; gradient vector flow snake model; stomach epidermis tumors segmentation; tumor cell recognition software system; Arithmetic; Biomedical engineering; Convergence; Epidermis; Equations; Force control; Image segmentation; Neoplasms; Stomach; Tumors; GVF; edge mapping; gauss blur; image segmentation; outside force field; snake model; stomach epidermis tumors;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Future BioMedical Information Engineering, 2008. FBIE '08. International Seminar on
  • Conference_Location
    Wuhan, Hubei
  • Print_ISBN
    978-0-7695-3561-6
  • Type

    conf

  • DOI
    10.1109/FBIE.2008.17
  • Filename
    5076780