Open Access Journal

ISSN : 2394-2320 (Online)

International Journal of Engineering Research in Computer Science and Engineering (IJERCSE)

Monthly Journal for Computer Science and Engineering

Open Access Journal

International Journal of Engineering Research in Computer Science and Engineering (IJERCSE)

Monthly Journal for Computer Science and Engineering

ISSN : 2394-2320 (Online)

Call For Paper : Vol 11, Issue 08, August 2024

Call for Paper

Vol 11, Issue 08, August 2024

Indexing

A Study of Brain Tumor MRI Image Using Distance Based Clustering Algorithms

Author : Dr. O.A. Mohamed Jafar 1 J. Zehara Sulthana 2

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Date of Publication :23rd January 2018

Abstract: Brain tumor segmentation is a challenging problem. The medical expert can analyze the MRI (Magnetic Resonance Imaging) but this activity is error-prone and time consuming process. Data clustering is the process of grouping similar objects. Existing brain tumor segmentation methods use Euclidean distance metric. In this paper, a study of hard and fuzzy methods based on different distance metrics – Manhattan and Chebyshev used for brain tumor segmentation is done. The result of Adaptive Fuzzy K-means (AFKM) is compared with K-means and Fuzzy K-means (FKM) algorithms. The AFKM based on Chebyshev distance gives better result than K-means and FKM techniques in terms of accuracy and quantitative measurements.

Reference :

    1. Kothari, “A Study on Classification and Detection of Brain Tumor Techniques”, International Journal of Computer Engineering and Technology, Vol. 6 (11), pp. 30-35, 2015.
    2. Deepa, Akansha Singh, “Review of Brain Tumor Detection from MRI Images”, IEEE 2016, pp. 3997- 4001.
    3. K.S. Angel Viji, J. Jayakumar, “Performance evaluation of standard image segmentation methods and clustering algorithms for segmentation of MRI brain tumor images”, European Journal of Scientific Research, Vol. 79, No. 2, pp. 166-179, 2012
    4. J. Vijay, J. Subhashini, “An Efficient Brain Tumor Detection Methodology Using K-Means Clustering Algorithm”, IEEE International Conference on Communication and Signal Processing, pp. 653-657, April 3-5, 2013.
    5. T. Logeswari and K. Karnan, “An improved implementation of brain tumor detection using segmentation based on soft computing”, Journal of Cancer Research and Experimental Oncology, Vol. 2, Issue 1, pp. 006-014, 2010.
    6. P. Vasuda and S. Satheesh, “Improved Fuzzy CMeans Algorithm for MR Brain Image Segmentation”, International Journal on Computer Science and Engineering (IJCSE), Vol. 02, No. 5, pp. 1713-1715, 2010.
    7. P. TamijeSelvy, V. Palanisamy and T. Purusothaman, “Performance Analysis of Clustering Algorithms in Brain Tumor Detection of MR Images”, European Journal of Scientific Research, Vol. 63, No. 3, pp. 321-330, 2011.
    8. Vishal B. Padole and D.S. Chaudhari, “A Review of Segmentation Methods for Detection of Brain Tumor in MRI”, International Journal of Electronics, Communication & Soft Computing Science and Engineering (IJECSCSE), Vol. 1, Issue 1, pp. 15-18, 2011.
    9. Rajesh C. Patil and Dr. A.S. Bhalchandra, “Brain Tumour Extraction from MRI Images Using MATLAB”, International Journal of Electronics, Communication & Soft Computing Science and Engineering (IJECSCSE), Vol. 2, No. 1, pp. 1-4, 2012.
    10. J. Vijay and J. Subhashini, “An Efficient Brain Tumor Detection Methodology Using K-Means Clustering Algorithm”, IEEE International Conference on Communication and Signal Processing, pp. 653-657, April 3-5, 2013.
    11. Alan Jose, S. Ravi and M. Sambath, “Brain Tumor Segmentation Using K-Means Clustering and Fuzzy C-Means Algorithms and its Area Calculation”, International Journal of Innovative Research in Computer and Communication Engineering, Vol. 2, Issue 3, pp. 3496-3501, 2014
    12. Vishal Shinde, PritiKine, SuchitaGadge and ShekharKhatal, “Brain Tumor Identification using MRI Images”, International Journal on Recent and Innovation Trends in Computing and Communication, Vol. 2, Issue 10, pp. 3050-3054, 2014. [
    13. V. Sagar Anil Kumar and T. Chandra Sekhar Rao, “Brain Tumor Extraction by K-means Clustering Based on Morphological Image Processing”, International Journal of Engineering and Computer Science, Vol. 3, Issue 10, pp. 8539-8546, 2014.
    14. Rabab Saadoon Abdoon, Loay Kadom Abood and S.M. Ali, “Analysis Study of Fuzzy C-Mean Algorithm Implemented on Abnormal MR Brain Images”, Vol. 7, Issue 3, pp. 42-49, 2015.
    15. Siddhi N. Nerurkar, “Brain Tumor Detection using Image Segmentation”, International Journal of Engineering Research in Computer Science and Engineering (IJERCSE), Vol. 4, Issue 4, pp. 64-70, 2017.
    16. K. Gayathri and D. Vasanthi, “Brain Tumor Segmentation Using K-Means Clustering and Fuzzy C-Means Algorithms”, International Journal of Scientific Research in Computer Science, Engineering and Information Technology, Vol. 2, Issue 2, pp. 704- 707, 2017.
    17. Azadeh Noori Hoshyar, Adel Al-Jumaily and Afsaneh Noori Hoshyar, “Comparing the Performance of Various Filters on Skin Cancer Images”, Procedia Computer Science, 42, pp. 32-37, 2014.
    18. Elena Deza and Michel Marie Deza, “Encyclopedia of Distances”, Springer, 2009.
    19. J. MacQueen, “Some methods for classification and analysis of multivariate observations”, Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, Vol. 1, pp. 281-297, 1967.
    20. H. Kashima, J. Hu, B. Ray and M. Singh, “K-means clustering of proportional data using L1 distance”, Proceedings of the 19th International Conference on Pattern Recognition, pp. 1-4, Dec. 8-11, 2008.
    21. YE Ping, “Fuzzy K-means algorithms based on membership function improvement”, Changchun Institute of Technology (Natural Sciences Edition), 2007.
    22. J.C. Bezdek, “Pattern recognition with fuzzy objective function algorithms”, Plenum Press, New York, 1981.
    23. S.N. Sulaiman and N.A.M. Isa, “Adaptive fuzzy-Kmeans clustering algorithm for image segmentation”, IEEE Transactions on Consumer Electronics, Vol. 56, pp. 2661-2668, 2010.
    24. Siti Noraini Sulaiman, Noreliani Awang Non, Iza Sazanita Isa, Norhazimi Hamzah, “Sementation of brain MRI Image based on clustering algorithm,”, Recent Advances in Electrical and Computer Engineering, pp. 236-241, 2014.
    25. N. Jadhav Swapnil and Prof. Sarita V. Verma, “Perceptible Performance of Different Clustering Techniques for Image Segmentation”, International Journal of Scientific Research Engineering & Technology (IJSRET), Vol. 3, Issue 5, pp. 902-906, 2014.
    26. Y.H. Yang and J. Liu, “Multiresolution Color Image Segmentation”, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 16, pp. 689- 700, 1994.
    27.  Zhou Wang, Alan Conrad Bovik, Hamid Rahim Sheikh, Eero P. Simoncelli, “Image Quality Assessment: From Visibility to Structural Similarity”, IEEE Transactions on Image Processing, Vol. 13, No. 4, pp. 600-612, 2004.

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