A fast underwater optical image segmentation algorithm based on a histogram weighted fuzzy c-means improved by PSO

Abstract

The S/N of an underwater image is low and has a fuzzy edge. If using traditional methods to process it directly, the result is not satisfying. Though the traditional fuzzy C-means algorithm can sometimes divide the image into object and background, its time-consuming computation is often an obstacle. The mission of the vision system of an autonomous underwater vehicle (AUV) is to rapidly and exactly deal with the information about the object in a complex environment for the AUV to use the obtained result to execute the next task. So, by using the statistical characteristics of the gray image histogram, a fast and effective fuzzy C-means underwater image segmentation algorithm was presented. With the weighted histogram modifying the fuzzy membership, the above algorithm can not only cut down on a large amount of data processing and storage during the computation process compared with the traditional algorithm, so as to speed up the efficiency of the segmentation, but also improve the quality of underwater image segmentation. Finally, particle swarm optimization (PSO) described by the sine function was introduced to the algorithm mentioned above. It made up for the shortcomings that the FCM algorithm can not get the global optimal solution. Thus, on the one hand, it considers the global impact and achieves the local optimal solution, and on the other hand, further greatly increases the computing speed. Experimental results indicate that the novel algorithm can reach a better segmentation quality and the processing time of each image is reduced. They enhance efficiency and satisfy the requirements of a highly effective, real-time AUV.

Keywords

underwater image / image segmentation / autonomous underwater vehicle (AUV) / gray-scale histogram / fuzzy C-means / real-time effectiveness / sine function / particle swarm optimization (PSO)

References

[1] Ahmed M.N., Yamany S.M., Mohamed N., Farag A.A., Moriarty T. A modified fuzzy C means algorithm for bias field estimation and segmentation of MRI data. IEEE Trans on Medical Imaging, 2002, 21(3): 193-199

[2] Bezdek J.C., Dubois D., Prade H. Fuzzy sets in approximate reasoning and information systems. Fuzzy Sets and Systems, 2001, 123(3): 405-406

[3] Dunn J.C. A fuzzy relative of the ISODATA process and its use in detecting compact well-separated clusters. Journal of Cybernetics, 1973, 3(3): 32-57

[4] Eberhart RC, Shi Y (2000). Comparing inertia weights and constriction factors in particle swarm optimization. Proceedings of the Congress on Evolutionary Computation, La Jolla, USA, 84–88.

[5] He R, Datta S, Sajja BR, Mehta M, Narayana PA (2004). Adaptive FCM with contextual constrains for segmentation of multi-spectral MRI. Proceedings of 26th IEEE Conference on Engineering in Medicine and Biology Society. San Francisco, USA, 1660–1663.

[6] Higasshi N, lba H (2003). Particle swarm optimization with Gaussian mutation. Proceedings of the Congress on Evolutionary Computation, Canberra, Australia, 72–79.

[7] Hu Xiaohui, Shi Yuhui, Eberhart RC (2004). Recent advances in particle swarm. Proceedings of the Congress on Evolutionary Computation, Oregon, Portland, 90–97.

[8] Li Xiang, Li Lihong, Lu Hongbing, Chen Dongqing, Liang Zengrong (2003). Inhomogeneity correction for magnetic resonance images with fuzzy C-mean algorithm. Proceedings of SPIE Conference on Medical Image Processing, San Diego, 995–1005.

[9] Luminia N. Fuzzy bagging: a novel ensemble of classifiers. Pattern Recognition, 2006, 39(3): 488-490

[10] Nikhil R.P., Bezdek J.C. On cluster validity for the fuzzy C-means model. IEEE Transactions on Fuzzy Systems, 2001, 3(3): 370-379

[11] Pham D.L., Prince J.L. Adaptive fuzzy segmentation of magnetic resonance images. IEEE Trans on Medical Imaging, 1999, 18(9): 737-752

[12] Rezaee M.R., van der Zwet P.M.J., Lelieveldt B.P.E., van der Geest R.J., Reiber J.H.C. A multiresolution image segmentation technique based on pyramidal segmentation and fuzzy clustering. IEEE Trans on Image Processing, 2000, 9(7): 1238-1248

[13] Sezgin M., Sankur B. Survey over image thresholding techniques and quantitative performance evaluation. Journal of Electronic Imaging, 2004, 13(1): 146-165

[14] Szilagyi L, Benyo Z, Szilagy SM, Adam HS (2003). MR brain image segmentation using an enhanced fuzzy C means algorithm. Proceedings of the 25th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Cancun, Mexico, 724–726.

[15] Xu Y., Xiao K. Technology development of autonomous ocean vehicle. Acta Automatica Sinica, 2007, 33(5): 518-521

[16] Yang S. Image pattern recognition with VC++ technology, 2005, Beijing: Tsinghua University Press & Beijing Jiaotong University Press, 161-162

[17] Yao Y., Xu Y. Parameter analysis of particle swarm optimization algorithm. Journal of Harbin Engineering University, 2007, 28(11): 1242-1246

[18] Yuan Xiaohai, Qiu Chenchang, Chen Rongsheng, Hu Zhen, Liu Peilin (1997). Vision system research for autonomous underwater vehicle. Proceedings of the IEEE International Conference on Intelligent Processing System, Beijing, 1465–1469.