Digital Medical Image Segmentation Using Fuzzy C-Means Clustering
DOI:
https://doi.org/10.21928/uhdjst.v4n1y2020.pp51-58Keywords:
Medical image, Medical image modality, Segmentation, Fuzzy C-means clusteringAbstract
In the modern globe, digital medical image processing is a major branch to study in the fields of medical and information technology. Every medical field relies on digital medical imaging in diagnosis for most of their cases. One of the major components of medical image analysis is medical image segmentation. Medical image segmentation participates in the diagnosis process, and it aids the processes of other medical image components to increase the accuracy. In unsupervised methods, fuzzy c-means (FCM) clustering is the most accurate method for image segmentation, and it can be smooth and bear desirable outcomes. The intention of this study is to establish a strong systematic way to segment complicate medical image cases depend on the proposed method to share in the decision-making process. This study mentions medical image modalities and illustrates the steps of the FCM clustering method mathematically with example. It segments magnetic resonance imaging (MRI) of the brain to separate tumor inside the brain MRI according to four statuses.
References
[1] A. Norouzi, M. S. M. Rahim, A. Altameem, T. Saba, A. E. Rad, A. Rehman and M. Uddin. “Medical image segmentation methods, algorithms, and applications”. IETE Technical Review, vol. 31, no. 3, pp. 199-213, 2014.
[2] R. C. Gonzalez and R. E. Woods. “Digital Image Processing”. 4th ed. Pearson Education, New York, 2018.
[3] R. Kumar, G. Satheesh and B. Nisha. “MRI brain image segmentation using Fuzzy C means cluster algorithm for tumor area measurement”. International Journal of Engineering Technology Science and Research, vol. 4, no. 9, pp. 929-935, 2017.
[4] T. Saikumar, P. Yugander, P. S. Murthy and B. Smitha. “Colour Based Image Segmentation Using Fuzzy C-Means Clustering”. In: International Conference on Computer and Software Modeling, Singapore, 2011.
[5] E. Doğanay, S. Kara, H. K. Özçelik and L. Kart. “A hybrid lung segmentation algorithm based on histogram-based fuzzy C-means clustering”. Journal Computer Methods in Biomechanics and Biomedical Engineering: Imaging and Visualization, vol. 6, no. 6, pp. 638-648, 2014.
[6] S. Naz, H. Majeed and H. Irshad. “Image Segmentation Using Fuzzy Clustering: A Survey”. In: 6th International Conference on Emerging Technologies, Islamabad, Pakistan, 2010.
[7] G. Padmavathi, M. M. Kumar and S. K. Thakur. “Nonlinear image segmentation using fuzzy c means clustering method with thresholding for underwater images”. IJCSI International Journal of Computer Science Issues, vol. 7, no. 3, pp. 35-40, 2010.
[8] O. Jamshidi and A. H. Pilevar. “Automatic segmentation of medical images using Fuzzy c-means and the genetic algorithm”. Journal Computational Medicine, vol. 2013, p. 972970, 2013.
[9] G. Stephanie. “Fuzzy Clustering Definition”, 2016. Available from: https://www.statisticshowto.datasciencecentral.com/fuzzy-clustering. [Last accessed on 2019 Oct 01].
[10] L. Ma, H. Chen, K. Meng and D. Liu. “Medical Image Segmentation Based on Improved Fuzzy C-Means Clustering”. In: International Conference on Smart Grid and Electrical Automation, Changsha, China, 2017.
[11] M. M. Ahmed and D. B. Mohamad. “Anisotropic diffusion model segmentation of brain MR images for tumor extraction by combining kmeans clustering and Perona-Malik”. International Journal of Image Processing, vol. 2, no. 1, pp. 27-34, 2008.
[12] J. Quintanilla-Dominguezab, B. Ojeda-Magañaac, M. G. Cortina- Januchsab, R. Ruelasc, A. Vega-Coronab and D. Andinaa. “Image segmentation by fuzzy and possibilistic clustering algorithms for the identification of microcalcifications”. Scientia Iranica, vol. 18, no. 3, pp. 580-589, 2011.
[13] H. Jiang, Y. Liu, F. Ye, H. Xi and M. Zhu. “Study of clustering algorithm based on Fuzzy C-means and immunological partheno genetic”. Journal of Software, vol. 8, no. 1, p. 134, 2013.
[14] M. Yambal and H. Gupta. “Image segmentation using Fuzzy C means clustering: A survey”. International Journal of Advanced Research in Computer and Communication Engineering, vol. 2, no. 7, pp. l-5, 2013.
[15] N. E. A. Khalid, N. M. Noor and N. Ariff. “Fuzzy c-means (FCM) for optic cup and disc segmentation with morphological operation”. Procedia Computer Science, vol. 42, pp. 255-262, 2014.
[16] N. A. Ali, B. Cherradi, A. E. Abbassi, O. Bouattane, M. Youssfi. “GPU Fuzzy c-means algorithm implementations: Performance analysis on medical image segmentation”. Multimedia Tools and Applications, vol. 77, no. 16, pp. 21221-21243, 2018.
[17] O. Bandyopadhyaya, A. Biswasa and B. B. Bhattacharyaba. “Long-bone fracture detection in digital X-ray images based on digital-geometric techniques”. Computer Methods and Programs in Biomedicine, vol. 123, pp. 2-14, 2016.
[18] S. K. Adhikari, J. K. Sing, D. Kumar, B. M. Nasipuri. “Conditional spatial fuzzy C-means clustering algorithm for segmentation of MRI images”. Applied Soft Computing, vol. 34, pp. 758-769, 2015.
[19] G. S. Chuwdhury, M. Khaliluzzaman and M. Rashed-Al-Mahfuz. “MRI Segmentation using Fuzzy C-Means Clustering and Bidimensional Empirical Mode Decomposition”. In: International Conference on Computer, Communication, Chemical, Materials and Electronic Engineering, 2016.
[20] E. N. Sathishkumar. “Fuzzy c Means Manual Work”. Lecturer at Periyar University, Salem, 2015.
[21] R. C. Gonzalez, R. E. Woods and S. L. Eddins. “Digital Image Processing Using MATLAB”. Pearson Education, London, United Kingdom, 2004.
[2] R. C. Gonzalez and R. E. Woods. “Digital Image Processing”. 4th ed. Pearson Education, New York, 2018.
[3] R. Kumar, G. Satheesh and B. Nisha. “MRI brain image segmentation using Fuzzy C means cluster algorithm for tumor area measurement”. International Journal of Engineering Technology Science and Research, vol. 4, no. 9, pp. 929-935, 2017.
[4] T. Saikumar, P. Yugander, P. S. Murthy and B. Smitha. “Colour Based Image Segmentation Using Fuzzy C-Means Clustering”. In: International Conference on Computer and Software Modeling, Singapore, 2011.
[5] E. Doğanay, S. Kara, H. K. Özçelik and L. Kart. “A hybrid lung segmentation algorithm based on histogram-based fuzzy C-means clustering”. Journal Computer Methods in Biomechanics and Biomedical Engineering: Imaging and Visualization, vol. 6, no. 6, pp. 638-648, 2014.
[6] S. Naz, H. Majeed and H. Irshad. “Image Segmentation Using Fuzzy Clustering: A Survey”. In: 6th International Conference on Emerging Technologies, Islamabad, Pakistan, 2010.
[7] G. Padmavathi, M. M. Kumar and S. K. Thakur. “Nonlinear image segmentation using fuzzy c means clustering method with thresholding for underwater images”. IJCSI International Journal of Computer Science Issues, vol. 7, no. 3, pp. 35-40, 2010.
[8] O. Jamshidi and A. H. Pilevar. “Automatic segmentation of medical images using Fuzzy c-means and the genetic algorithm”. Journal Computational Medicine, vol. 2013, p. 972970, 2013.
[9] G. Stephanie. “Fuzzy Clustering Definition”, 2016. Available from: https://www.statisticshowto.datasciencecentral.com/fuzzy-clustering. [Last accessed on 2019 Oct 01].
[10] L. Ma, H. Chen, K. Meng and D. Liu. “Medical Image Segmentation Based on Improved Fuzzy C-Means Clustering”. In: International Conference on Smart Grid and Electrical Automation, Changsha, China, 2017.
[11] M. M. Ahmed and D. B. Mohamad. “Anisotropic diffusion model segmentation of brain MR images for tumor extraction by combining kmeans clustering and Perona-Malik”. International Journal of Image Processing, vol. 2, no. 1, pp. 27-34, 2008.
[12] J. Quintanilla-Dominguezab, B. Ojeda-Magañaac, M. G. Cortina- Januchsab, R. Ruelasc, A. Vega-Coronab and D. Andinaa. “Image segmentation by fuzzy and possibilistic clustering algorithms for the identification of microcalcifications”. Scientia Iranica, vol. 18, no. 3, pp. 580-589, 2011.
[13] H. Jiang, Y. Liu, F. Ye, H. Xi and M. Zhu. “Study of clustering algorithm based on Fuzzy C-means and immunological partheno genetic”. Journal of Software, vol. 8, no. 1, p. 134, 2013.
[14] M. Yambal and H. Gupta. “Image segmentation using Fuzzy C means clustering: A survey”. International Journal of Advanced Research in Computer and Communication Engineering, vol. 2, no. 7, pp. l-5, 2013.
[15] N. E. A. Khalid, N. M. Noor and N. Ariff. “Fuzzy c-means (FCM) for optic cup and disc segmentation with morphological operation”. Procedia Computer Science, vol. 42, pp. 255-262, 2014.
[16] N. A. Ali, B. Cherradi, A. E. Abbassi, O. Bouattane, M. Youssfi. “GPU Fuzzy c-means algorithm implementations: Performance analysis on medical image segmentation”. Multimedia Tools and Applications, vol. 77, no. 16, pp. 21221-21243, 2018.
[17] O. Bandyopadhyaya, A. Biswasa and B. B. Bhattacharyaba. “Long-bone fracture detection in digital X-ray images based on digital-geometric techniques”. Computer Methods and Programs in Biomedicine, vol. 123, pp. 2-14, 2016.
[18] S. K. Adhikari, J. K. Sing, D. Kumar, B. M. Nasipuri. “Conditional spatial fuzzy C-means clustering algorithm for segmentation of MRI images”. Applied Soft Computing, vol. 34, pp. 758-769, 2015.
[19] G. S. Chuwdhury, M. Khaliluzzaman and M. Rashed-Al-Mahfuz. “MRI Segmentation using Fuzzy C-Means Clustering and Bidimensional Empirical Mode Decomposition”. In: International Conference on Computer, Communication, Chemical, Materials and Electronic Engineering, 2016.
[20] E. N. Sathishkumar. “Fuzzy c Means Manual Work”. Lecturer at Periyar University, Salem, 2015.
[21] R. C. Gonzalez, R. E. Woods and S. L. Eddins. “Digital Image Processing Using MATLAB”. Pearson Education, London, United Kingdom, 2004.
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Published
2020-02-27
How to Cite
Mohammed, B. A., & Al-Ani, M. S. (2020). Digital Medical Image Segmentation Using Fuzzy C-Means Clustering. UHD Journal of Science and Technology, 4(1), 51–58. https://doi.org/10.21928/uhdjst.v4n1y2020.pp51-58
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