The Different of Digital Image segmentation Techniques: A Review
 |
International Journal of Computer Trends and Technology (IJCTT) | 
|
| © 2017 by IJCTT Journal | ||
| Volume-49 Number-2 |
||
| Year of Publication : 2017 | ||
| Authors : Nirgish Kumar, Dr. Vivek Srivastava | ||
| DOI : 10.14445/22312803/IJCTT-V49P112 |
Nirgish Kumar, Dr. Vivek Srivastava "The Different of Digital Image segmentation Techniques: A Review". International Journal of Computer Trends and Technology (IJCTT) V49(2):76-82, July 2017. ISSN:2231-2803. www.ijcttjournal.org. Published by Seventh Sense Research Group.
Abstract -
The different of digital image
segmentation is the way toward apportioning an
image into numerous portions, in order to change
the portrayal of an image into something that is
more important and simpler to examine. A few
universally useful calculations and strategies have
been produced for image segmentation. This paper
depicts the diverse segmentation systems utilized as
a part of the field of ultrasound and SAR Image
Processing. Firstly this paper examines and gathers
a portion of the advances utilized for image
segmentation. At that point, a bibliographical study
of current segmentation strategies is given in this
paper lastly broad propensities in image
segmentation are displayed.
References
[1] K. Gunna, “Implementation and Comparison of
Different Segmentation Techniques for Medical
Images,” vol. 134, no. 2, pp. 5–9, 2016.
[2] N. M. Zaitoun and M. J. Aqel, “Survey on Image
Segmentation Techniques,” Procedia - Procedia
Comput. Sci., vol. 65, no. Iccmit, pp. 797–806, 2015.
[3] N. Tokas, S. Karkra, and M. K. Pandey,
“Comparison of Digital Image Segmentation
Techniques- A Research Review,” vol. 5, no. 5, pp.
215–220, 2016.
[4] A. Kaur, R. Kumar, and K. Kainth, “Review
Paper on Image Segmentation Techniques,” vol. 6,
no. 7, pp. 336–339, 2016.
[5] A. C. Study, O. N. Image, and S. Techniques,
“Available Online through,” vol. 8, no. 2, pp. 12712–
12717, 2016.
[6] Kalpana Shrivastava, Neelesh Gupta and Neetu
Sharma–Medical Image Segmentation using
Modified K Means Clustering? International Journal
of Computer Applications (0975 – 8887) Volume 103
– No 16, October 2014
[7] S. Karkra ,J.B. Patel “ Atlas based medical
segmentation techniques-A review”Ge-international
journal of engineering research vol-3,issues-5,may
2015.
[8] J. Yuan, D. Wang, and R. Li, "Remote sensing
image segmentation by combining spectral and
texture features," IEEE Transactions on Geoscience
and Remote Sensing, vol. 52, pp. 16-24, 2014.
[9] N. M. Noor, J. C. Than, O. M. Rijal, R. M.
Kassim, A. Yunus, A. A. Zeki, et al., "Automatic
Lung Segmentation Using Control Feedback System:
Morphology and Texture Paradigm," Journal of
medical systems, vol. 39, pp. 1-18, 2015.
[10] D. Reska, C. Boldak, and M. Kretowski, "A
Texture-Based Energy for Active Contour Image
Segmentation," in Image Processing &
Communications Challenges 6, ed: Springer, 2015,
pp. 187-194.
[11] I. A. Yusoff, N. A. M. Isa, and K. Hasikin,
"Automated two dimensional K-means clustering
algorithm for unsupervised image segmentation,"
Computers & Electrical Engineering, vol. 39, pp.
907-917, 2013.
[12] L. Sørensen, M. Nielsen, P. Lo, H. Ashraf, J. H.
Pedersen, and M.De Bruijne, "Texture-based analysis
of COPD: a data-driven approach," IEEE
Transactions on Medical Imaging, vol. 31, pp.70-78,
2012.
[13] H. Cao, H.-W. Deng, and Y.-P. Wang,
"Segmentation of M-FISH images for improved
classification of chromosomes with an adaptive
Fuzzy C-Means Clustering Algorithm," IEEE
Transactions on Fuzzy Systems, vol. 20, pp. 1-8,
2012.
[14] M. Gong, Y. Liang, J. Shi, W. Ma, and J. Ma,
"Fuzzy c-means clustering with local information and
kernel metric for image segmentation," IEEE
Transactions on Image Processing, vol. 22, pp. 573-
584, 2013.
[15] H. Narkhede, "Review of image segmentation
techniques," Int. J. Sci. Mod. Eng, vol. 1, p. 28, 2013
Keywords
Segmentation Techniques, MR Image,
Ultrasound Images.