A Novel Fuzzy C—Mean Based Segmentation Technique for Spinal Cord Tumors from MR Images


  • Alam N. Shaikh, Nisha A. Auti, B. K. Sarkar


Brain tumor, image segmentation, Fuzzy C Means algorithm, Magnetic Resonance Image.


Image processing plays a crucial role in extracting meaningful information from images to enhance their utility and effectiveness. Among various techniques, image segmentation stands out as an efficient method for extracting and isolating specific features within images. This research focuses on optimizing the Fuzzy C Means (FCM) algorithm for accurately identifying the axial and coronal planes in MRI brain images, considering both the algorithm's accuracy and computational efficiency.

The preprocessing phase involves converting MRI brain images from DICOM format to a standard image format. To enhance image quality, a Gaussian filter technique is applied to eliminate noise. Subsequently, the FCM algorithm is implemented to segment regions affected by brain tumours in MR images. The evaluation of algorithmic efficiency and accuracy involves comparing histogram values of images before and after segmentation with the cluster center values determined by the FCM algorithm.

The results provide insights into the algorithm's performance, with a focus on computational time as a key metric. By identifying the best fit of the FCM algorithm for both axial and coronal planes, this research contributes to advancing the field of image segmentation in the context of brain tumor detection. In conclusion, the study underscores the significance of FCM algorithm in accurately delineating tumor-affected regions in MRI brain images, thereby aiding in the diagnosis and treatment of brain tumors. The identified optimal parameters showcase the potential of FCM as a valuable tool in the realm of medical image analysis.


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Bezdek, J. C. (2023). Pattern Recognition with Fuzzy Objective Function Algorithms. New York: Plenum Press.

Pham, D. L., Xu, C., & Prince, J. L. (2023). Current methods in medical image segmentation. Annual Review of Biomedical Engineering, 2, 315-337.

Pal, N. R., & Pal, S. K. (2022). A review on image segmentation techniques. Pattern Recognition, 26(9), 1277-1294.

Krishnapuram, R., & Keller, J. M. (2022). A possibilistic approach to clustering. IEEE Transactions on Fuzzy Systems, 1(2), 98-110.

Lin, C. T., & Lee, C. S. G. (2016). Neural Fuzzy Systems: A Neuro-Fuzzy Synergism to Intelligent Systems. Upper Saddle River, NJ: Prentice-Hall.

González, R. C., & Woods, R. E. (2008). Digital Image Processing. Upper Saddle River, NJ: Pearson Prentice Hall.

Ganesh Kumar, P., & Sivanandam, S. N. (2007). Introduction to Fuzzy and Neural Control. New Delhi: Springer.

Njeh, C. F. (2008). Tumor delineation: The weakest link in the search for accuracy in radiotherapy. Journal of Medical Physics, 33(4), 136-140.

Haralick, R. M., Shanmugam, K., & Dinstein, I. (1973). Textural features for image classification. IEEE Transactions on Systems, Man, and Cybernetics, SMC-3(6), 610-621.

Van Griethuysen, J. J., Fedorov, A., Parmar, C., Hosny, A., Aucoin, N., Narayan, V., ... & Aerts, H. J. (2017). Computational radiomics system to decode the radiographic phenotype. Cancer Research, 77(21), e104-e107.

Jain, A. K., & Dubes, R. C. (1988). Algorithms for Clustering Data. Upper Saddle River, NJ: Prentice-Hall.

Pal, N. R., Pal, S. K., & Mitra, P. (1992). Multispectral image segmentation using the rough-fuzzy clustering approach. Pattern Recognition Letters, 13(6), 467-474.




How to Cite

Nisha A. Auti, B. K. Sarkar, A. N. S. . (2024). A Novel Fuzzy C—Mean Based Segmentation Technique for Spinal Cord Tumors from MR Images. International Journal of Intelligent Systems and Applications in Engineering, 12(21s), 291–295. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/5421



Research Article