Image segmentation of pulmonary parenchyma can be detected from multisliced CT images using image segmentation. It can be modeled as a nonlinear multimodal global optimization problem. The traditional 2D Otsu algorithm, though effective, is quite time consuming for determining the optimum threshold values. In this paper we propose a combination of 2D Otsu method with modified ABC algorithm (called Adaptive ABC or AABC) to reduce the response and computational time. The proposed method has been implemented and tested on three images. Experimental results show the competence of the proposed method for selecting the optimum threshold.

1. Singh A. An artificial bee colony algorithm for the leaf constrained minimum spanning tree problem. Applied Soft Computing 2009;9:625–31.
2. Kang F, et al. Structural inverse analysis by hybrid simplex artificial bee colony algorithms. Computers & Structures 2009;87:861–70.
3. Samrat L, et al. Artificial bee colony algorithm for small signal model parameter extraction of MESFET. Engineering Applications of Artificial Intelligence 2010;11:1573–2916.
4. Karaboga D, Basturk B. A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm. Journal of Global Optimization 2007;39:171–459.
5. Karaboga D, Basturk B. On the performance of artificial bee colony (ABC) algorithm. Applied Soft Computing 2008;8:687–97.
6. Karaboga D, Basturk B. A comparative study of artificial bee colony algorithm. Applied Mathematics and Computation 2009;214:108–32.
7. Q.K. Pan, M.F. Tasgetiren, P.N. Suganthan, T.J. Chua, A discrete artificial bee colony algorithm for the lot-streaming flow shop scheduling problem, Information Sciences 181 (12) (2011) 2455–2468.
8. S. Sundar, A. Singh, A swarm intelligence approach to the quadratic minimum spanning tree problem, Information Sciences 180 (17) (2010) 3182–3191.
9. F. Kang, J. Li, Q. Xu, Structural inverse analysis by hybrid simplex artificial bee colony algorithms, Computers & Structures 87 (13-14) (2009) 861–870.
10. HE Zhi-ming,MA Miao. Fast Image Matching Approach Based on Grey Relational Analysis and Artificial Bee Colony Algorithm. Computer technology and development. 2010. Vol.20(10),78-81( in Chinese).
11. M. E. Lee, S. H. Kim, W. H. Cho, "Segmentation of Brain MR Images using an Ant Colony Optimization algorithm," Ninth IEEE International Conference on Bioinf. andBioeng., pp. 366-369, 2009.
12. J. C. Bezdek, L.O. Hall, L. P. Clarke, "Review of MR image segmentation techniques using pattern recognition," Med. Phys., vol.20, No. 4, pp. 1033-1048, 1993.
13. Gonzalez, Rafael C. & Woods, Richard E. (2002). Thresholding. In Digital Image Processing, pp. 595–611. Pearson Education.
14. A.S. Abutaleb , Automatic thresholding of gray-level pictures using two dimensional entropy. Comput. Vision, Graphics, Image Process. 47, pp. 22-32(1989).
15. P. Suetens, E. Bellon, D. Vandermeulen, M. Smet, G. Marchal, J.Nuyts, L. Mortelman, "Image segmentation: methods and applications in diagnostic radiology and nuclear medicine," European Journal of Radiology, vol. 17, pp. 14-21, 1993.
16. J. F. Brenner, J. M. Lester, W. D. Selles, "Scene segmentation in automated histopathology: techniques evolved from cytology automation," Pattern Recognition, vol. 13, pp. 65-77, 1981.
17. K. Lim, A. Pfefferbaum, "Segmentation of MR brain images into cerebrospinal fluid spaces, white and gray matter," J. Comput. Assist. Tomogr., vol. 13, pp. 588-593, 1989.
18. A. Goshtasby, D. A. Turner, "Segmentation of Cardiac Cine MR Images for extraction of right and left ventricular chambers," IEEE Trans. Med. Imag., vol. 14, No. 1, pp. 56-64, 1995.
19. D. Brzakovic, X. M. Luo, P. Brzakovic, "An approach to automated detection of tumors in mammograms," IEEE Trans. Med. Imag., vol. 9, No. 3, pp. 233-241, 1990.
20. Wang Lei, Shen Ting-zhi . Two-Dimensional Entropy Method Based on Genetic Algorithm. Journal of Beijing Institute of Technology,No.2,pp.50-57 (2002) (in Chinese).
21. Frucci, Maria; Sanniti di Baja, Gabriella (2008). "From Segmentation to Binarization of Gray-level Images". Journal of Pattern Recognition Research 3 (1): 1-13.
22. Zhenghong, Pan Li. The automatic selection of image threshold on the basis of genetic algorithm. journal of image and graphics (in Chinese) vol 4(A), No .4 1999 327~330.
23. Ye zhiwei, Zhengzhaobao, Yu Xin, Ningxiaogang. Automatic threshold selection based on ant colony optimization. 2005ICNN&B'05, Beijing,pp.728-732.
24. Mehmet Sezgin Bu¨ lent Sankur Survey over image thresholding techniques and quantitative performance evaluation Journal of Electronic Imaging 13(1), 146–165.
25. Sharma, T.K., Pant, M.Enhancing the food locations in an Artificial Bee Colony algorithm, In Proc. of Swarm Intelligence (SIS), 2011 IEEE Symposium, pp. 119 – 124, 2011.
26. R.Helen, N.Kamaraj, K.Selvi, V.Raja Raman," Segmentation of Pulmonary Parenchyma in CT lung Images based on 2D Otsu optimized by PSO" ICETECT 2011.

2d Otsu Abc Thresholding Image Segmentation