Author : S.Shyni Carmel Mary 1
Date of Publication :29th March 2018
Abstract: Research on Medical Image proposes an efficient platform for automatic analysis and detection of any Deformations in a given medical image data set especially in Spinal Cord for an effective and better understanding of diagnosis. The abnormality of the spinal cord may include Tumor, Disc a hernia, Fracture, swelling etc., which has been detected from any given modality of Medical images such as MRI, CT, and fMRI etc. In this work, Automated Decision support system is introduced for fast and accurate analysis which will help to confirm the existence of affected part of the Spinal Cord MR image. It has two phases. Phase I: Identifying any anomaly features or distortion is found to have existed in the given image or not by using histograms. Phase II: Involves in Clustering of the image which is used to find the depth of the existence of the calcification in the MRI Spinal Image. The performance of the algorithm and the time taken to complete every cluster phase is analyzed. Further, the algorithm’s efficiency is being observed to prove that it gives a perfect accuracy
Reference :
-
- S.Shyni Carmel Mary,S. Sasikala. Survey on Segmentation Techniques for Spinal Cord Images,International Journal of Data Mining Techniques and Applications, Vol. 5, Issue 2, 2016, pp. 121-124, DOI: 10.20894/IJDMTA.102.005.002.005, ISSN: 2278-2419
- Larhmam, Mohamed Amine, Mohammed Benjelloun, and Saïd Mahmoudi. "Vertebra identification using template matching modelmp and $ $ K $ $ K-means clustering." International journal of computer assisted radiology and surgery 9, no. 2 (2014): 177-187.
- Patel, Piyush M., Brijesh N. Shah, and Vandana Shah. "Image segmentation using K-mean clustering for finding tumor in medical application." International Journal of Computer Trends and Technology (IJCTT) 4, no. 5 (2013): 1239-1242.
- Isa, Nor Ashidi Mat. "Automated edge detection technique for Pap smear images using moving K-means clustering and modified seed based region growing algorithm." International Journal of The Computer, the Internet and Management 13, no. 3 (2005): 45-59.
- Vijayalakshmi, P., K. Selvamani, and M. Geetha. "Segmentation of brain MRI using k-means clustering algorithm." Int. J. Eng. Trends Technol 3 (2011): 113-115.
- Vijay, K., and K. Selvakumar. "Brain fMRI clustering using interaction K-means algorithm with PCA." In Communications and Signal Processing (ICCSP), 2015 International Conference on, pp. 0909-0913. IEEE, 2015.
- Wu, Ming-Ni, Chia-Chen Lin, and Chin-Chen Chang. "Brain tumor detection using color-based k-means clustering segmentation." In Intelligent Information Hiding and Multimedia Signal Processing, 2007. IIHMSP 2007. Third International Conference on, vol. 2, pp. 245-250. IEEE, 2007.
- Dubey, Shiv Ram, Pushkar Dixit, Nishant Singh, and Jay Prakash Gupta. "Infected fruit part detection using K-means clustering segmentation technique." Ijimai 2, no. 2 (2013): 65-72.