An Efficient Fuzzy Technique For Detection Of Brain Tumor

P.G.K. Sirisha*, R. Pradeep Kumar Reddy**, C. Naga Raju***
* Assistant Professor, CSE Department, SMIT Engineering College.
**-*** Associate Professor, CSE Department, YSR Engineering College.
Periodicity:April - June'2013
DOI : https://doi.org/10.26634/jse.7.4.2316

Abstract

In this epoch Medical Image segmentation is one of the most challenging problems in the research field of MRI scan image classification and analysis. The importance of image segmentation is to identify various features of the image that are used for analyzing, interpreting and understanding of images. Image segmentation for MRI of brain is highly essential due to accurate detection of brain tumor. This paper presents an efficient image segmentation technique that can be used for detection of tumor in the Brain. This innovative method consists of three steps. First is Image enhancement to improve the quality of the tumor image by eliminating noise and to normalize the image. Second is fuzzy logic which produce optimal threshold to avoid the fuzziness in the image and makes good regions regarding Image and tumor part of the Image. Third is novel OTSU technique applied for separating the tumor regions in the MRI. This method has produced better results than traditional extended OTSU method.

Keywords

Fuzziness, Segmentation, OTSU, Weight, Crisp Set, Tumor

How to Cite this Article?

P.G.K. Sirisha, R. Pradeep Kumar and C. Naga Raju (2013). An Efficient Fuzzy Technique For Detection Of Brain Tumor. i-manager’s Journal on Software Engineering, 7(4), 13-17. https://doi.org/10.26634/jse.7.4.2316

References

[1]. C. Naga Raju, C. Hari Krishna, T. Siva Priya, (2012). “Design of Primaty Screening Tool for Early Detection of Brest Cancer”, in Journal of Advances in Information Technology, Vol. 3, No. 4, November, pp. 228-235.
[2]. R.C. Gonzalez and R.E. Woods, (2002). Digital Image Processing (Prentice Hall, 2002).
[3]. Dr. Vipul Singh, (2013). Digital Image Processing with MATLAB and Lab VIEW; ELSEVIER 2013.
[4]. S. Jayaraman, S. Esakkirajan, T. Veerakumar, (2009). Digital Image Processing, Tata McGraw Hill Education Private Limited.
[5]. Guang Yang, Kexiong Chen, Maiyu Zhou, Zhonglin Xu, Yongtian Chen, (2007). “Study on Statistics Iterative Thresholding Segmentation Based on Aviation Image”, Vol.2, pp187-188, 8th ACIS International Conference on software Engineering, Artificial Intelligence, Networking, and Parallel/ Distributed Computing.
[6]. A.S. Abutaleb, “Automatic Thresholding of Gray-Level Pictures using Two Dimensional Entropy”,
[7]. Salem Saleh Al-amri, N.V. Kalyankar and Khamitkar S.D. (2010). “Image Segmentation by using Thershod Techniques”, Journal of Computing, Vol.2, pp 83-86, 2010.
[8]. Khang Siang Tan, Nor Ashidi Mat Isa, (2011). “color Image Segmentation using Histogram Thresholding-Fuzzy C-means hybrid approach”, Pattern Recognition, vol44,pp 1-15.
[9]. Dr. G. Padmavathi, M. Muthukumar, Suresh Kumar Thakur, (2010). “Non Linear Image Segmentation using Fuzzy C-means clustering method with thresholding for underwater images”, IJCSI, Vol 7, pp. 35-40.
[10]. N. Otsu, (1978). A threshold selection method from gray-level histogram, IEEE Transactions on Systems Man Cybernet, SMC-8, pp. 62-66.
[11]. H.F. Ng, (2006). “Automatic thresholding for defect detection”, Pattern Recognition Letters, (27): 1644-1649.
[12]. M. Sezgin and B. Sankur, (2003). Survey over image thresholding techniques and quantitative performance evaluation, Journal of Electronic Imaging, pp.146-156.
[13]. H. Lee and R.H. Park, (1990). Comments on an optimal threshold scheme for image segmentation, IEEE Trans. Syst.Man Cybern, SMC-20, 741-742.
[14]. J.Z. Liu and W. Q. Li, (1993). The Automatic thresholding of gray-level picture via two-dimensional Otsu method, Acta Automatica Si.19, 101-105.
If you have access to this article please login to view the article or kindly login to purchase the article

Purchase Instant Access

Single Article

North Americas,UK,
Middle East,Europe
India Rest of world
USD EUR INR USD-ROW
Pdf 35 35 200 20
Online 35 35 200 15
Pdf & Online 35 35 400 25

Options for accessing this content:
  • If you would like institutional access to this content, please recommend the title to your librarian.
    Library Recommendation Form
  • If you already have i-manager's user account: Login above and proceed to purchase the article.
  • New Users: Please register, then proceed to purchase the article.