International Journal of Modern Education and Computer Science (IJMECS)

ISSN: 2075-0161 (Print), ISSN: 2075-017X (Online)

Published By: MECS Press

IJMECS Vol.9, No.7, Jul. 2017

Computational Approach to Image Segmentation Analysis

Full Text (PDF, 524KB), PP.30-37

Views:120   Downloads:8


Gourav, Tejpal Sharma, Harsmeet Singh

Index Terms

Image processing;Image division;Segmentation methods


Image division refers to the way toward dividing an advanced picture into various portions. Image division says to a parcel of an image into various divisions that are homogeneous or comparable. The objective of the division is to Simplify or potentially changes the portrayal of an image into something that is more important and simpler to dissect. Advancement of precise image division different image division strategies is utilized to take care of a particular issue. The motivation behind this survey is to give an overview of various image division methods. These methods are sorted into four sorts: an) Edge based division b) Threshold Segmentation c) Clustering-based division D) Region-based division. This survey tended to different image division methods, their correlation and presents the issues identified with those procedures.

Cite This Paper

Gourav, Tejpal Sharma, Harsmeet Singh,"Computational Approach to Image Segmentation Analysis", International Journal of Modern Education and Computer Science(IJMECS), Vol.9, No.7, pp.30-37, 2017.DOI: 10.5815/ijmecs.2017.07.04


[1]Y. Saraf, “Algorithms for Image Segmentation,” p. 30, 2006.

[2]Z. Cebeci and F. Yildiz, “Comparison of K-Means and Fuzzy C-Means Algorithms on Different Cluster Structures,” J. Agric. Informatics, vol. 6, no. 3, pp. 13–23, 2015.

[3]R. R. and K. Tejaswini, “A Survey of Image Segmentation Algorithms Based On Fuzzy Clustering,” Int. J. Comput. Sci. Mob. Comput., vol. 2, no. July, pp. 200–206, 2013.

[4]R. Maini and H. Aggarwal, “Study and comparison of various image edge detection techniques,” Int. J. Image Process., vol. 3, no. 1, pp. 1–11, 2009.

[5]K. K. Singh and A. Singh, “A study of image segmentation algorithms for different types of images,” Int. J. Comput. Sci., vol. 7, no. 5, pp. 414–417, 2010.

[6]U. G. Nevagi, “Edge Detection Techniques : A Survey,” vol. 5, no. 2, pp. 274–281, 2016.

[7]P. Kamavisdar, S. Saluja, and S. Agrawal, “A survey on image classification approaches and techniques,” Int. J. Adv. …, vol. 2, no. 1, pp. 1005–1009, 2013.

[8]C. Science and M. Studies, “A Survey on Image Segmentation Techniques and Clustering,” vol. 7782, no. December, pp. 45–51, 2013.

[9]D. Kaur and Y. Kaur, “Various Image Segmentation Techniques: A Review,” Int. J. Comput. Sci. Mob. Comput., vol. 3, no. 5, p. 809–814, date accessed: 18/05/2016, 2014.

[10]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.

[11]R. Yogamangalam and B. Karthikeyan, “Segmentation Techniques Comparison in Image Processing,” vol. 5, no. 1, pp. 307–313, 2013.

[12]W. Khan, “Image Segmentation Techniques: A Survey,” J. Image Graph., vol. 1, no. 4, pp. 166–170, 2013.

[13]A. K. Chaubey, “Comparison of The Local and Global Thresholding Methods in Image Segmentation,” no. 1, pp. 1–4, 2016.

[14]C. Pantofaru and M. Hebert, “A Comparison of Image Segmentation Algorithms,” Robotics, vol. 2, no. CMU-RI-TR-05-40, pp. 123–130, 2005.

[15]C. P. M. H. Cmu-ri-tr- and C. P. M. H. Cmu-ri-tr-, “A Comparison of Image Segmentation Algorithms,” Robotics, p. 336, 2005.

[16]P. Malji and S. Sakhare, “Survey on Methodologies and Techniques Involved in Feature Selection,” vol. 4, no. 3, pp. 275–280, 2016.

[17]“a Survey of Various,” pp. 273–279, 2015.

[18]A. Manikannan and J. Senthilmurugan, “A Comparative Study about Region Based and Model Based Using Segmentation .Techniques,” pp. 1948–1950, 2015.

[19]N. M. Zaitoun and M. J. Aqel, “Survey on Image Segmentation Techniques,” Procedia Comput. Sci., vol. 65, no. Iccmit, pp. 797–806, 2015.

[20]K. Camilus and V. Govindan, “A review on graph based segmentation,” Int. J. Image, vol. 4, no. 4, pp. 3194–3197, 2012.

[21]R. Dass and S. Devi, “Image Segmentation Techniques,” Int. J. Electron. Commun. Technol., vol. 3, no. 1, pp. 66–70, 2012.

[22]S. Saini and K. Arora, “A Study Analysis on the Different Image Segmentation,” Int. J. Inf. Comput. Technol., vol. 4, no. 14, pp. 1445–1452, 2014.

[23]A. Kauri and P. Kaur, “A Comparative Review of Various Segmentation Techniques for Early Detection of Exudates in Retinal Fundus Images,” pp. 1–4, 2016.

[24]E. P. Thakur and E. N. Madaan, “a Survey of Image Segmentation Techniques,” Int. J. Res. Comput. Appl. Robot., vol. 24, no. 4, pp. 158–165, 2014.

[25]S. Chen and H. Leung, “Chaotic spread spectrum watermarking for remote sensing images,” J. Electron. Imaging, vol. 13, no. 1, p. 220, 2004.

[26]S. S. Al-Amri, N. V. Kalyankar, and S. D. Khamitkar., “Image segmentation by using edge detection,” Int. J. Comput. Sci. Eng., vol. 2, no. 3, pp. 804–807, 2010.

[27]P. D. Raju and G. Neelima, “Image Segmentation by using Histogram Thresholding,” Ijcset, vol. 2, no. 1, pp. 776–779, 2012.

[28]P. S. N. Holambe and P. G. Kumbhar, “Comparison between Otsu ’ s Image Thresholding Technique and Iterative Triclass,” vol. 33, no. 2, 2016.

[29]C. Science, W. B. Mikhael, and C. Science, “Overview of current Biomedical Image segmentation methods,” pp. 803–808, 2016.

[30]A. Sharma, “Image Segmentation using Firefly Algorithm,” 2016.

[31]A. Khadidos, V. Sanchez, C. Li, and S. Member, “Weighted Level Set Evolution Based on Local Edge Features for Medical Image Segmentation,” vol. 7149, no. c, 2017.

[32]Y. Li, Y. Guo, Y. Kao, and R. He, “Image Piece Learning for Weakly Supervised Semantic Segmentation,” pp. 1–12, 2016.

[33]S. Lu and L. Lei, “A Hybrid Extraction-Classification Method For Brain Segmentation In MR Image,” pp. 1381–1385, 2016.

[34]X. Yang, C. Guo, and A. S. S. Method, “Parallel Spatial-Domain Liver Segmentation of CT Abdominal Images.”

[35]D. R. I. Xpdq, S. Phwkrg, J. Wkh, K. Dffxudf, and S. L. Q. Vhfwlrq, “6hpl dxwrpdwhg 9huwheudo 6hjphqwdwlrq ri +xpdq 6slqh lq 05, ,pdjhv,” pp. 14–16, 2016.

[36]Z. Wang and A. F. C. M. F. Field, “A Novel Natural Image Segmentation Algorithm based on Markov Random Field and Improved Fuzzy C-Means Clustering Method,” no. 1.

[37]R. Pemula, “Segmentation using Fuzzy Multi-Region Technique.”

[38]S. Mirghasemi, P. Andreae, M. Zhang, and R. Rayudu, “Severely Noisy Image Segmentation via Wavelet Shrinkage Using PSO and Fuzzy C-Means,” 2016.

[39]H. Wu, Y. Wu, and S. Zhang, “Cartoon Image Segmentation Based on Improved SLIC Superpixels and Adaptive Region Propagation Merging,” pp. 277–281, 2016.

[40]L. Chen and Z. Wang, “Nearly Lossless HDR Images Compression by Background Image Segmentation,” pp. 241–246, 2016.

[41]H. Leonardo and B. Jos, “An AutoAssociative Neural Network for Image Segmentation,” 2016.

[42]A. Wong-od, A. Rodtook, S. Rasmequan, and K. Chinnasarn, “Automated segmentation of media-adventitia and lumen from intravascular ultrasound images using non-parametric thresholding,” pp. 220–225, 2017.

[43]Y. Linsen, L. Yanjun, C. Deyun, and L. Peng, “A Spatially Compact Mixture Model for Image Segmentation,” pp. 470–473, 2016.

[44]V. N. Vasyukov and A. Y. Zaitseva, “Segmentation of Textured Images Described by Hierarchical Gibbs Model,” pp. 452–455, 2016.

[45]S. Qian and G. Weng, “Medical Image Segmentation Based on FCM And Level Set Algorithm ( I,” no. Figure Id, pp. 225–228, 2016.