Image Segmentation Using K- Means Clustering Method for Brain Tumour Detection

  • Authors

    • S Thylashri
    • Udutha Mahesh Yadav
    • T Danush Chowdary
    2018-04-17
    https://doi.org/10.14419/ijet.v7i2.19.15058
  • Segmentation, clustering, Brain tumour, k-means, Magnetic Resonance Imaging (MRI)
  • Abstract

    Brain tumour is an irregular development by cells imitating among them in an unstoppable way. Specific identification of size and area of Brain tumour assumes a fundamental part in the analysis of tumour. Image processing is a dynamic research territory in which processing of image in medical field is an exceedingly difficult field. Segmentation of image assumes a critical part in handling of image as it helps in the finding of suspicious districts from the restorative image. In this paper a proficient algorithm is proposed for detection of tumour based on segmentation of brain by means of clustering technique. The main idea in this clustering algorithm is to transfer  a given gray-level image and then separate  tumour objects position  from other items of an MR image by using K-means clustering. Experiments say that segmentation for MR brain images can be done to help medical professionals to identify exactly size and region of the tumour located area in brain.

     

     

  • References

    1. [1]. A.R.Kavitha,Dr.C.Chellamuthu, Ms.KavinRupa, "An Efficient Approach for Brain Tumour Detection Based on Modified Region Growing and Network in MRIImages,†IEEE, 2012.

      [2]. S.Roy and S.K.Bandyopadhyay, "Detection and Quantification of Brain Tumor from MRI of Brain and its Symmetric Analysisâ€, International Journal of Information and Communication Technology Research, vol. 2, 2012.

      [3]. AjalaFunmilola A, Oke O.A, Adedeji T.O, Alade O.M, Oyo Adewusi E.A, “Fuzzy k-c-means Clustering Algorithm for Medical Image Segmentation||â€, Journal of Information Engineering and Applications, ISSN 2224-5782 (print) , ISSN 2225-0506 (online), Vol 2, No.6, 2012.

      [4]. David Rivest-Henault, Mohamed Cheriet, "Unsupervised MRI segmentation of brain tissues using a local linear model and set,"Elsevier, 2011.

      [5]. T. Logeswari, M.Karnan, "An Improved Implementati7 of Brain Tumor Detection using Segmentation Based on Hierarchical Self Organizing Map,†IEEE, 2010.

      [6]. Brijesh Shah, Satish Shah, Y P Kosta,"Novel Improved Fuzzy C-Mean Algorithm MR-images Segmentation,"IJSCE,2010

      [7]. P.Vasuda, S.Satheesh, "Improved Fuzzy C-Means Algorithm for MR Brain Image Segmentationâ€, in International Journal on Computer Science and Engineering(1JCSE), vol. 02, no. 05, pp. 1713-1715, 2010.

      [8]. A. Sheila and T. Ried, "Interphase Cytogenetics of Sputum Cells for the Early Detection of Lung Carcinogenesis||â€, Journal of Cancer Prevention Research, vol. 3, no. 4, pp. 416-419, March, 2010.

      [9]. El-Sayed Ahmed El-Dahshan, Tamer Hosny, AbdelBadeehM.Salem, “Hybrid intelligent techniques for

      MRI Brain Images classification," Elsevier Itd, 2009

  • Downloads

  • How to Cite

    Thylashri, S., Mahesh Yadav, U., & Danush Chowdary, T. (2018). Image Segmentation Using K- Means Clustering Method for Brain Tumour Detection. International Journal of Engineering & Technology, 7(2.19), 97-100. https://doi.org/10.14419/ijet.v7i2.19.15058

    Received date: 2018-07-04

    Accepted date: 2018-07-04

    Published date: 2018-04-17