• DocumentCode
    3586086
  • Title

    MRI brain tumor recognition using Modified Shuffled Frog Leaping Algorithm

  • Author

    Ladgham, Anis ; Sakly, Anis ; Mtibaa, Abdellatif

  • Author_Institution
    Electr. Dept., Nat. Sch. of Eng. of Monastir, Ibn Eljazzar, Tunisia
  • fYear
    2014
  • Firstpage
    504
  • Lastpage
    507
  • Abstract
    This paper presents a novel optimal algorithm for MRI brain tumor recognition. To do this, we use the newly developed meta-heuristic MSFLA (Modified Shuffled Frog Leaping Algorithm). Otherwise, a suitable choice of the fitness function ensures faster time of research with greater chance of convergence to the optimal value. The calculation of the used fitness function is linked to the image. The image must be scanned to calculate this function. For this, this function assists to quickly discover the adequate area modeling the tumor. Computer simulation results illustrate the effectiveness of the developed algorithm.
  • Keywords
    biomedical MRI; evolutionary computation; medical image processing; object recognition; tumours; MRI brain tumor recognition; fitness function; magnetic resonance imaging; metaheuristic MSFLA; modified shuffled frog leaping algorithm; Algorithm design and analysis; Genetic algorithms; Image recognition; Image segmentation; Magnetic resonance imaging; Optimization; Tumors; Fitness function; MRI tumor; MSFLA;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Sciences and Techniques of Automatic Control and Computer Engineering (STA), 2014 15th International Conference on
  • Type

    conf

  • DOI
    10.1109/STA.2014.7086694
  • Filename
    7086694