• DocumentCode
    677125
  • Title

    A hybrid model for extraction of brain tumor in MR images

  • Author

    Vidyarthi, Ankit ; Mittal, Natasha

  • Author_Institution
    Dept. of Comput. Eng., Malaviya Nat. Inst. of Technol., Jaipur, India
  • fYear
    2013
  • fDate
    12-14 Dec. 2013
  • Firstpage
    202
  • Lastpage
    206
  • Abstract
    Brain tumor is one of the most life- threatening diseases in the field of medical science. A proper diagnosis of such a disease is required in its early phase of its generation. Various methods have been proposed for the extraction of the abnormality regions from the brain tumor Magnetic Resonance (MR) images. However, most of the methods proposed in the literature have drawback of losing background image. In this paper, a hybrid model is proposed which identifies the region of interest using fused results of threshold segmentation and morphological operations. Initially, an abnormal brain MR image is processed with Otsu threshold based segmentation and morphological operations like erosion. Further, both the segmented resultant images are fused with the original MR image to preserve the background and correctly identification of the tumor region.
  • Keywords
    biomedical MRI; brain; diseases; feature extraction; image segmentation; medical image processing; tumours; MR images; Otsu threshold based segmentation; abnormal brain MR image; abnormality regions; brain tumor extraction; brain tumor magnetic resonance images; hybrid model; life threatening diseases; medical science; morphological operations; Clustering algorithms; Image segmentation; Magnetic resonance imaging; Medical diagnostic imaging; Morphological operations; Tumors; Brain Tumor; MR images; Morphological Operations; Threshold segmentation;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Signal Processing and Communication (ICSC), 2013 International Conference on
  • Conference_Location
    Noida
  • Print_ISBN
    978-1-4799-1605-4
  • Type

    conf

  • DOI
    10.1109/ICSPCom.2013.6719783
  • Filename
    6719783