Abstract
As the images are examined using the latest machine learning process, the techniques for computing the images become highly essential. This computation applied over the images allows one to have an assessable information's or values from the images. Since segmentation plays a vital role in processing of images by enhancing or hypothetically altering the images making the examination of valuable insights easier. Several procedures and the methods for segmenting the images have been developed. However to have an better process it is important to sort out an effective segmentation procedure, so the paper performs the analysis of the clustering based image segmentation techniques applied on the magnetic resonance image of the human brain to detect the white matter hyper intensities part. The evaluation process take place in the MATLAB to evince the accurate valuation procedure. The optimal procedure is sorted out to be used in observing and examining the medical images by implementing over a computer assisted tool.
References
Heena, A., N. Biradar, and N. M. Maroof. "A novel approach to review various image segmentation techniques." Int. J. Innov. Res. Comput. Commun. Eng. ISO Certif. Organ 32975, no. 2 (2007): 266-269.
Papadopoulos, Symeon, Christos Zigkolis, Giorgos Tolias, Yannis Kalantidis, Phivos Mylonas, Yiannis Kompatsiaris, and Athena Vakali. "Image clustering through community detection on hybrid image similarity graphs." In 2010 IEEE International Conference on Image Processing, pp. 2353-2356. IEEE, 2010.
Brickman, Adam M., Karen L. Siedlecki, Jordan Muraskin, Jennifer J. Manly, José A. Luchsinger, Lok-Kin Yeung, Truman R. Brown, Charles DeCarli, and Yaakov Stern. "White matter hyperintensities and cognition: testing the reserve hypothesis." Neurobiology of aging 32, no. 9 (2011): 1588-1598.
Suganya, R., and R. Shanthi. "Fuzzy c-means algorithm-a review." International Journal of Scientific and Research Publications 2, no. 11 (2012): 1.
Ajala Funmilola, A., O. A. Oke, T. O. Adedeji, O. M. Alade, and E. A. Adewusi. "Fuzzy kc-means clustering algorithm for medical image segmentation." Journal of Information Engineering and Applications, ISSN 22245782 (2012): 2225-0506.
Wardlaw, Joanna M., Maria C. Valdés Hernández, and Susana Muñoz‐Maniega. "What are white matter hyperintensities made of? Relevance to vascular cognitive impairment." Journal of the American Heart Association 4, no. 6 (2015): e001140.
Jeyavathana, R. Beaulah, R. Balasubramanian, and A. Anbarasa Pandian. "A survey: analysis on preprocessing and segmentation techniques for medical images." International Journal of Research and Scientific Innovation (IJRSI) (2016).
Isa, I. S., S. N. Sulaiman, N. Md Tahir, M. Mustapha, and N. K. A. Karim. "A new technique for K-means cluster centers initialization of WMH segmentation." In Proceedings of the International Conference on Imaging, Signal Processing and Communication, pp. 45-49. 2017.
Koresh, M. H., and J. Deva. "Computer vision based traffic sign sensing for smart transport." J. Innovative Image Process.(JIIP) 1, no. 01 (2019): 11-19.
Chandy, Abraham. "A Review On Iot Based Medical Imaging Technology For Healthcare Applications." Journal of Innovative Image Processing (JIIP) 1, no. 01 (2019): 51-60.
Bindhu, V. "Biomedical Image Analysis Using Semantic Segmentation." Journal of Innovative Image Processing (JIIP) 1, no. 02 (2019): 91-101.
Kumar, T. Senthil. "A Novel Method for HDR Video Encoding, Compression and Quality Evaluation." Journal of Innovative Image Processing (JIIP) 1, no. 02 (2019): 71-80.