Liejun Wang1, Song Zhang 1 and Yanqing Qi1
1College of Information Science and Engineering, Xinjiang University, Urumqi, P.R. China
Received:
June 20, 2016
Accepted:
September 5, 2017
Publication Date:
December 1, 2017
Download Citation:
||https://doi.org/10.6180/jase.2017.20.4.12
ABSTRACT
In recent years, the pulse coupled neural network (PCNN) is widely used in image segmentation, but the existing algorithm has an unsatisfactory performance with the definition of single threshold, especially in uneven illumination or low contrast image. In this paper, in order to eliminate the impact of the illumination and improve the adaptability, deblocking PCNN algorithm is utilized. It segments the image into several rectangles with the same size. Then PCNN algorithm is applied to segment each block and stopped by improved OSTU. The emulation experiment shows that this method is better than traditional image segmentation method in uneven illumination or low contrast image.
Keywords:
PCNN, OSTU, Image Segmentation
REFERENCES
- [1] Gao,C.,Zhou,D.andGuo,Y.,“AnIterativeThresholding Segmentation Model Using a Modified Pulse Couple NeuralNetwork,” NeuralProcessing Letters,Vol.39,No. 1, pp. 8195 (2014). doi: 10.1007/s11063-013-9291-z
- [2] Subashini, M. M. and Sahoo, S. K., “Pulse Coupled Neural Networks and Its Applications,” Expert Systems with Applications, Vol. 41, pp. 39653974 (2014). doi: 10.1016/j.eswa.2013.12.027
- [3] Harris, M. A., Van, A. N., Malik, B. H., Jabbour, J. M. and Maitland, K. C., “APulse Couple Neural Network Segmentation Algorithm for Reflectance Confocal Images of Epithelial Tissue,”PlosOne,Vol.10, No. 3,pp. e0122368 (2015). doi: 10.1371/journal.pone.0122368
- [4] Wang, Z., Wang, S., Zhu, Y. and Ma, Y., “Review of Image Fusion Based on Pulse-coupled Neural Network,” Archives of Computational Methods in Engineering, Vol. 23, No. 4, pp. 659671 (2016). doi: 10.1007/ s11831-015-9154-z
- [5] Chen, Y., Park, S. K., Ma, Y. and Ala, R., “ANew Automatic Parameter Setting Method of a Simplified PCNN for Image Segmentation,” Neural Networks IEEE Transactions on, Vol. 22, pp. 880892 (2011). doi: 10.1109/TNN.2011.2128880
- [6] Wei, S., Hong, Q. and Hou, M., “Automatic Image Segmentation Based on PCNN with Adaptive Threshold Time Constant,” Neurocomputing, Vol. 74, pp. 1485–1491(2011).doi:10.1016/j.neucom.2011.01.005
- [7] Chen, Y. L., Park, S. K., Ma,Y. D. and Ala, R., “ANew Automatic Parameter Setting Method of a Simplified PCNN for Image Segmentation,” Neural Networks, IEEE Transactions on, Vol. 22, No. 6, pp. 880892 (2011). doi: 10.1109/TNN.2011.2128880
- [8] Zhao, H. and Ding, S., “Study of Automated PCNN System Based on Fruit Fly Optimization Algorithm,” Journal of Computational Information Systems, Vol. 10, No. 15, pp. 66356642 (2014).
- [9] Rava, T., Bettaiah, V. and Ranganath, H. S., “Adaptive Pulse Coupled Neural Network Parameters for Image Segmentation,” World Acad Sci Eng Technol, Vol. 73, pp. 10461052 (2011).
- [10] Li, J., Zou, B., Ding, L. and Gao, X., “Image Segmentation with PCNN Model and Immune Algorithm,” Journal of Computers, Vol. 8, pp. 24292436 (2013).
- [11] Gao,C.,Zhou,D.andGuo,Y.,“AutomaticIterativeAlgorithm for Image Segmentation Using a Modified Pulsecoupled Neural Network,” Neurocomputing, Vol. 119, pp. 332338 (2013). doi: 10.1016/j.neucom.2013.03.025
- [12] Zhou, D., Gao, C. and Guo, Y., “ACoarse-to-fine Strategy for Iterative Segmentation Using Simplified Pulsecoupled Neural Network,” Soft Computing, Vol. 18, pp. 557570 (2014). doi: 10.1007/s00500-013-1077-8
- [13] Yoshihara, M. and Kurokawa, H., “Hardware Implementationof IC-PCNN for theColor ImageSegmentation,” Region 10 Symposium, IEEE, pp. 391396 (2014). doi: 10.1109/TENCONSpring.2014.6863064
- [14] Otsu, N., “A Threshold Selection Method from Graylevel Histograms,” IEEE Systems, Man and Cybernetics Society, Vol. 9, pp. 6269 (1979). doi: 10.1109/ TSMC.1979.4310076
- [15] Singla, A. and Patra, S., “A Fast Automatic Optimal Threshold Selection Technique for Image Segmentation,” Signal Image & Video Processing, Vol. 11, No. 2,pp.243250(2017).doi:10.1007/s11760-016-0927-0
- [16] Liu, J. and Wang, C., “An Algorithm for Image Binarization Based on Adaptive Threshold,” Control and Decision Conference, Ccdc, Vol. 9, pp. 39583962 (2009). doi: 10.1109/CCDC.2009.5191959
- [17] Zheng, X., Tan, W. and Du, J., “A Fast Adaptive Binarization Method Based on Sub Block OSTU and Improved Sauvola,” Wireless Communications, Networking and Mobile Computing (WiCOM), 2011 7th International Conference on, pp. 15 (2011). doi: 10. 1109/wicom.2011.6040579
- [18] Zhong, Y., Liu, W., Zhao, J. and Zhang, L., “Change Detection Based on Pulse-coupled Neural Networks and the NMI Feature for High Spatial Resolution RemoteSensing Imagery,” IEEE Geoscience and Remote Sensing Society, Vol. 12, pp. 537541 (2015). doi: 10. 1109/LGRS.2014.2349937
- [19] Ma, Y. D., Liu, Q. and Qian, Z. B., “Automated Image Segmentation Using Improved PCNN Model Based on Cross-entropy,” Intelligent Multimedia, Video and Speech Processing, 2004. Proceedings of 2004 International Symposium on, pp. 743746 (2005). doi: 10.1109/ISIMP.2004.1434171
