This is an open access article published by the IET under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0/)
The importance of image processing is increasing in the digitally connected world due to its numerous applications in various fields of medical science, astronomy, weather prediction and video surveillance systems etc. The latest research and development in this field has helped the authors to obtain finer details of a particular image under study. The image segmentation technique, a part of digital image processing, helps to obtain meaningful information of the object. This study discusses the three widely used important image segmentation techniques: namely, split and merge, image growing and thresholding and their effects on a sample image. The authors results thus depict a significant difference in the segmented image by split and merge, image growing and thresholding. Split and merge is the optimal method of image segmentation as compared with the other two techniques mentioned above. The choice of the method varies with type of image, its colour, intensity and noise level.
References
-
-
1)
-
6. Samet, H.: ‘The quadtree and related hierarchical data structures’, Comput. Surv., 1984, 16, (2) (doi: 10.1145/356924.356930).
-
2)
-
9. Gonzalez, R.C., Woods, R.E.: ‘Digital image processing’ (Pearson Education, Inc., Publishing as Prentice-Hall, 2008, 3rd edn.).
-
3)
-
1. Zhang, Y.J.: ‘Evaluation and comparison of different segmentation algorithms’, Pattern Recognit. Lett., 1997, 18, (10), pp. 963–974 (doi: 10.1016/S0167-8655(97)00083-4).
-
4)
-
11. Grady, L.: ‘Random walks for image segmentation’, IEEE Trans. Pattern Anal. Mach. Intell., 2006, 28, (11), pp. 1–17 (doi: 10.1109/TPAMI.2006.233).
-
5)
-
3. Wu, W., Chen, A.Y.C., Zhao, L., et al: ‘Brain tumor detection and segmentation in a CRF framework with pixel-pairwise affinity and super pixel-level features’, Int. J. Comput. Aided Radiol. Surg., 2014, 9, pp. 241–253 (doi: 10.1007/s11548-013-0922-7).
-
6)
-
4. Delmerico, J.A., David, P., Corso, J.J.: ‘Building façade detection, segmentation and parameter estimation for mobile robot localization and guidance’. Int. Conf. on Intelligent Robots and Systems, 2011, pp. 1632–1639.
-
7)
-
7. Ohlander, R., Price, K., Reddy, D.R.: ‘Picture segmentation using a recursive region splitting method’, Comput. Graph. Image Process., 1978, 8, (3), pp. 313–333, (doi: 10.1016/0146-664X(78)90060-6).
-
8)
-
11. Gevers, T., Kajcovski, V.K.: ‘Pattern recognition’. Conf. A: A Computer Vision, Image processing, Proc. of the 12th IAPR Conf., IEEE, vol. 1, 1994.
-
9)
-
2. Pham, D.L., Xu, C., Prince, J.L.: ‘Current methods in medical image segmentation’, Annu. Rev. Biomed. Eng., 2000, 2, pp. 315–337, .
-
10)
-
10. Lee, D.T., Schachter, B.J.: ‘Two algorithms for constructing a Delaunay triangulation’, Int. J. Comput. Inf. Sci., 1980, 9, (3) (doi: 10.1007/BF00977785).
-
11)
-
12. Khalifa, A.R.: ‘Evaluating the effectiveness of region growing and edge detection segmentation algorithms’, J. Am. Sci., 2010, 6, (10).
-
12)
-
8. Chen, L.: ‘The lambda-connected segmentation and the optimal algorithm for split-and-merge segmentation’, Chin. J. Comput., 1991, 14, pp. 321–331.
http://iet.metastore.ingenta.com/content/journals/10.1049/joe.2015.0171
Related content
content/journals/10.1049/joe.2015.0171
pub_keyword,iet_inspecKeyword,pub_concept
6
6