Manuscript Number : CSEIT172133
Brain Tumor Image Segmentation using K-means Clustering Algorithm
Authors(2) :-Kanchana Devi E, Dr. D. Devi Aruna
Brain tumor segmentation aims to separate the different tumor tissues such as active cells, necrotic core, and edema from normal brain tissues of White Matter (WM), Gray Matter (GM), and Cerebrospinal Fluid (CSF). MRI based brain tumor segmentation studies are attracting more and more attention in recent years due to non-invasive imaging and good soft tissue contrast of Magnetic Resonance Imaging (MRI) images. With the development of almost two decades, the innovative approaches applying computer-aided techniques for segmenting brain tumor are becoming more and more mature and coming closer to routine clinical applications. The purpose of this paper is to provide a K-means clustering algorithm for MRI-based brain tumor segmentation. K-means clustering algorithm is an unsupervised algorithm and it is used to segment the interest area from the background. However, before applying K-means algorithm, first partial stretching enhancement is applied to the image to improve the quality of the image.
Image Segmentation, K-Means Clustering, Magnetic Resonance Imaging (MRI), Segmentation
- K. Atsushi, N. Masayuki, "K-Means Algorithm Using Texture Directionality for Natural Image Segmentation”, IEICE technical report. Image engineering, 97 (467), pp.17-22, 1998.
- A. Murli, L. D’Amore, V.D. Simone, "The Wiener Filter and Regularization Methods for Image Restoration Problems”, Proc. The 10th International Conference on Image Analysis and Processing, pp. 394-399, 1999.
- S. Ray, R.H. Turi, "Determination of number of clusters in K-means clustering and application in colthe image segmentation”, Proc. 4th ICAPRDT, pp. 137-143, 1999.
- T. Adani, H. Ni, B. Wang, "Partial likelihood for estimation of multiclass posterior probabilities”, Proc. the IEEE International Conference on Acoustics, Speech, and Signal Processing, Vol. 2, pp. 1053-1056, 1999.
- B. Kovesi, J.M. Boucher, S. Saoudi, "Stochastic K-means algorithm for vector quantization”, Pattern Recognition Letters, Vol. 22, pp. 603-610, 2001.
- J. Z. Wang, J. Li, G. Wiederhold, "Simplicity: Semantics-sensitive integrated matching for picture libraries”, IEEE Transactions on Pattern Analysis and Machine Intelligence, 23 (9), pp. 947–963, 2001.
- Y. Gdalyahu, D. Weinshall, M. Wermen, "Self-Organizationin Vision: Stochastic clustering for Image Segmentation, Perceptual Grouping, and Image database Organization”, IEEE Transactions on Pattern Analysis and Machine Intelligence,Vol.23, No. 12, pp. 1053- 1074, 2001.
- C. Carson, H. Greenspan, "Blobworld: Image Segmentation Using Expectation-Maximization and Its Application to Image Querying”, IEEE Transactions On Pattern Analysis And Machine Intelligence, Vol. 24, No. 8, pp. 1026-1038, 2002.
- C. J. Veenman, M.J.T. Reinders, E. Backer, "A maximum variance cluster algorithm”, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 24, No. 9, pp. 1273-1280, 2002.
- B. Wei, Y. Liu, Y. Pan, "Using Hybrid Knowledge Engineering and Image Processing in Color Virtual Restoration of Ancient Murals”, IEEE Transactions on Knowledge and Data Engineering, Vol. 15, No. 5, 2003.
Published in : Volume 2 | Issue 1 | January-February 2017
Date of Publication : 2017-02-28
License: This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 209-214
Manuscript Number : CSEIT172133
Publisher : Technoscience Academy
URL : http://ijsrcseit.com/CSEIT172133