• DocumentCode
    230927
  • Title

    Automatic brain MRI image segmentation using FCM and LSM

  • Author

    Singh, Prashant ; Bhadauria, H.S. ; Singh, Ashutosh

  • Author_Institution
    Dept. of Comput. Sci. & Eng., G.B. Pant Eng. Coll., Pauri Garhwal, India
  • fYear
    2014
  • fDate
    8-10 Oct. 2014
  • Firstpage
    1
  • Lastpage
    6
  • Abstract
    The significant objective of this paper is to produce a method that is able to delineate the object of interest or tumor region easily from the available brain MRI images. This is attained by the unification of the fuzzy c-means clustering and level set method. The method proposed performs the segmentation by smoothly exploiting the spatial function during FCM clustering. Since, we are utilizing the FCM which could prove the automaticity of the method by dividing the original image into clusters and then using one cluster for automatic initialization. This in turn helps in making the whole processing less tedious with reducing the time as well. Thereby, if considered it could be competent tool in future. Secondly, to find the contour of tumor region in the original image the proposed method uses the level set method which comes in handy in situations where the topologies of the images changes frequently by merging or splitting in two. Also, the proposed methodology makes use of variational level method in place of generic level set method which in turn eliminates one more flaw of re- initializing the contour during segmentation. When we are using the segmentation methods which are manual then it can lead to a situation where different medical experts generate different results which can also overcome by using the proposed approach.
  • Keywords
    biomedical MRI; brain; fuzzy set theory; medical image processing; tumours; FCM clustering; LSM; automatic brain MRI image segmentation; fuzzy c-means clustering; level set method; spatial function; tumor region; variational level method; Biomedical imaging; Clustering algorithms; Image segmentation; Level set; Magnetic resonance imaging; Shape; Tumors; Fuzzy c-means; Image segmentation; defuzzification; level set methods; variational level sets;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Reliability, Infocom Technologies and Optimization (ICRITO) (Trends and Future Directions), 2014 3rd International Conference on
  • Conference_Location
    Noida
  • Print_ISBN
    978-1-4799-6895-4
  • Type

    conf

  • DOI
    10.1109/ICRITO.2014.7014706
  • Filename
    7014706