Improved Fuzzy C-Means Algorithm for Brain Tumor Identification Analysis Using Magnetic Resonance Brain Images

Authors

  • Isselmou Abd El kader Department of Biomedical Engineering, Hebei University of Technology, Tianjin, China;
  • Shuai Zhang Department of Biomedical Engineering, Hebei University of Technology, Tianjin, China;
  • Guizhi Xu Department of Biomedical Engineering, Hebei University of Technology, Tianjin, China;

DOI:

https://doi.org/10.14738/jbemi.43.3110

Keywords:

MRI, Segmentation, Tumor Identification, FCM algorithm, Accuracy, DOI, TC.

Abstract

Image processing plays a very important role in the analysis images of different standards; it supports the doctor’s decision and helps to easily diagnose the patient. In this paper we processed the magnetic resonance brain images, which is the most advanced medical images using the improved fuzzy c-means algorithm, this process consists of two stages, the first stage of tumor identification in the resonance brain images and the second stage analysis of the algorithm performance using accuracy detection, TC, DOI, sensitivity and specificity, the improved fuzzy c-means algorithm has given excellent results in the terms of efficiency and performance values.

 

References

(1) Nandha Gopal, “Automatic detection of brain tumor trough MR image”, Int, J.Adv.Res. Comput. Commun. Eng, 2 (April) ((4)) (2013).

(2) Andac Hamamci, Nadir Kucuk, Kutlay Karaman, Kayihan Engin and Gozde Unal, “Tumor-Cut: Segmentation of Brain Tumors on Contrast-Enhanced MR Images for Radiosurgery Applications”, IEEE transactions on medical imaging, vol. 31 (2011), 790 – 804

(3) Karan Sikka, Nitesh Sinha, Pankaj K. Singh and Amit K. Mishra, A fully automated Algorithm under Modified FCM framework for improved brain MR image segmentation, Magnetic resonance Imaging, 27 (2009), 994–1004

(4) R. S. PankajSapra, ShivaniKhurana, "Brain Tumor Detection Using Neural Network," International journal of Science and Modern Engineering (IJISME), vol. 1, August 2013.

(5) Rafael C. Gonzalez, Digital Image Processing, Second Edition Ed, The United States of America: University of Tennessee, 2001

(6) Sun. W, Segmentation method of MRI using fuzzy Gaussian basis neural network, Neural Information Processing, 8 (2) (2005), 19–24.

(7) Nan Zhang, Su Rua, St駱hane Lebonvallet, Qingmin Liao and Yuemin Zhu, Kernel Feature selection To fuse multi-spectral MRI images for brain tumor segmentation, Computer Vision and image Understanding, 115 (2011) 256–269.

(8) Rasoul Khayati, Mansur V afadusta, Farzad Towhidkhaha, and S.Massood Nabavi, Fully Automatic Segmentation of multiple sclerosis lesions in brain MR FLAIR images using Adaptive mixtures method and Markov random field model, Computers in Biology and Medicine, 38 (2008), 379 – 390.

Downloads

Published

2017-07-14

How to Cite

Abd El kader, I., Zhang, S., & Xu, G. (2017). Improved Fuzzy C-Means Algorithm for Brain Tumor Identification Analysis Using Magnetic Resonance Brain Images. Journal of Biomedical Engineering and Medical Imaging, 4(3), 15. https://doi.org/10.14738/jbemi.43.3110