Image Segmentation Techniques: A Survey

Authors

  • Bhagwat M Dept. of Comp. Sci. & Engg ,Kalinga University, Raipur, India
  • Pise V Dept. of Electronics Engg,RCERT, Chandrapur, India

Keywords:

Image segmentation, Techniques, Visualization

Abstract

The process of image segmentation is defined as the technique via which a given image is segmented into several objects, in order to further analyze objects present in the image [9]. The popularity of image segmentation is because of its importance in the area of image processing. The prime task of the researchers working in the field is to develop a method for efficient and better image segmentation. There are certain factors that affect the process of image segmentation like the intensity of image to be segmented, color, type and the noise present in the image [12]. No algorithm has been developed till date that could keep a look at all the above listed factors and then segment the image effectively so that all the problems that can come in the way of image segmentation can be avoided. This paper presents a review of some of the techniques developed for image segmentation.

References

[1] Chang-T sunli, Randy Chiao, “Multi-resolution genetic clustering algorithm for texture segmentation image and vision computing.” 2003,

[2] International conference on advance computer theory and engineering. (2010): image segmentation algorithm research and improvement.

[3] Kim, Eun Wi, Jung Keechul, “Genetic algorithm for video segmentation[J]”. Pattern recognition, January, 2005, 59-73.

[4] R. manavalan and K. Thangabel (2011), “TRUS image segmentation using morphological operators DBSCAN clustering.”

Mei Yean Choong, Wei YeangKow, YitKwong Chin, “Image segmentation via normalized cuts and clustering algorithm.” 2012 IEEE international conference on control system.

[5] Suganya1, Menaka, “Various Segmentation Techniques in Image Processing: A Survey,” IJIRCCE, Vol.2, Special Issue 1, March 2014.

[6] DibyaJyoti Bora (September, 2014) “A Novel Approach Towards Clustering Based Image Segmentation”, IJESE, Volume-2 Issue-11, pp 6-10.

[7] Yon Deng, Xuemei Cui, Shaolong Wu, “An improved image segmentation algorithm based on the watershed transform.”

[8] Rajiv Kumar (December, 2011) “Image Segmentation using Discontinuity-Based Approach”, IJMIP, Volume 1, Issues ¾, pp 72-78.

[9] AmanpreetKaur , “A Review Paper on Image Segmentation and its Various Techniques in Image Processing”, IJSR, Volume 3 Issue 12, December 2014, pp 12-14

[10] Muhammad Waseem Khan et al, “A Survey: Image Segmentation

Techniques”, International Journal of Future Computer and

Communication, Vol. 3, No. 2, April 2014, pp 89-93

[11] RohanKandwal, “Review: Existing Image Segmentation Techniques”, IJARCSSE, Volume 4, Issue 4, April 2014, pp 153-156

[12] Rajiv Bansal, Sona Kajla, “A Survey on Various Techniques for Image Segmentation’’, IJIRCSSE Vol. 4, Issue 4, April 2016

[13] Swapan Samaddar, Dr. A RamaSwamy Reddy, “Comparative Study Of Image Segmentation Techniques On Chronic Kidney Diseases,” IJPAM, Volume 118 No. 14 2018, 235-239.

[14] S.C.Zhu and A.Yuille, “Region competition: unifying snakes, region growing and Bayes/MDL for multiband image segmentation.” IEEE transaction on pattern analysis and machine intelligence, vol.18 september,1996, PP 884-900

[15] L.J.Shapiro and G.C.Stockman. Computer vision prentice hall,2001

[16] Nameirakpamdhanachandra,Khumanthemmanglam,“Image segmentation using k-means clustering algorithm and subtractive clustering algorithm”. 11th international multi-conference on information processing-2015(IMCIP-2015).

[17] G. Eason, B. Noble, and I.N. Sneddon, “On certain integrals of Lipschitz-Hankel type involving products of Bessel functions,” Phil. Trans. Roy.Soc. London, vol. A247, pp. 529-551, April 1955.

Downloads

Published

2025-11-25

How to Cite

[1]
M. Bhagwat and V. Pise, “Image Segmentation Techniques: A Survey”, Int. J. Comp. Sci. Eng., vol. 7, no. 11, pp. 90–93, Nov. 2025.

Issue

Vol. 7 No. 11 (2019): IJCSE Special Issue May Edition

Section

Survey Article

License

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Authors contributing to this journal agree to publish their articles under the Creative Commons Attribution 4.0 International License, allowing third parties to share their work (copy, distribute, transmit) and to adapt it, under the condition that the authors are given credit and that in the event of reuse or distribution, the terms of this license are made clear.