基于邻域搜索JADE的二维Otsu多阈值图像分割

doi:10.3969/j.issn.1001-506X.2020.10.03

Abstract

Abstract:

In order to further improve the segmentation accuracy and speed up the segmentation, a 2D Otsu multi-threshold image segmentation scheme based on the adaptive differential evolution algorithm with optional external archive, with neighborhood search (JADE-GL for short). Firstly, aiming at the problem of the original JADE algorithm such as slow convergence speed and easy to fall into local optimum, an improved mutation strategy based on neighborhood radius search is proposed to improve the global exploration and development ability of the algorithm. Then, the propesed algorithm is compared with existing segmentation methods and other JADE variant algorithms for 2D Otsu multi-threshold segmentation comparison experiments. Finally, the performance of the algorithm is quantitatively analyzed by function convergence curve, segmentation distance measure and peak signal to noise ratio (PSNR). The experimental results show that the proposed algorithm has more significant advantages in terms of convergence speed, segmentation accuracy and the effect of segmentation images as the number of thresholds increases.

Key words: image segmentation, 2D Otsu, multi-threshold, neighborhood search, adaptive differential evolution algorithm with optional external archive (JADE)

CLC Number:

TP391.41

Jun LUO, Jianqiang LIU, Yanan PANG. Multi-threshold image segmentation of 2D Otsu based on neighborhood search JADE[J]. Systems Engineering and Electronics, 2020, 42(10): 2164-2171.

Figures/Tables 8

Fig.1

Fig.2

Fig.3

Table 1

Fig.4

Fig.5

Table 2

Data comparison of five algorithms"

算法		Lena(512×512)			Car(512×512)			Plane(512×512)
算法		双阈值	三阈值	四阈值	双阈值	三阈值	四阈值	双阈值	三阈值	四阈值
JADE	距离测度	4 842.77	4 906.97	4 924.38	6 775.94	6 822.67	6 820.92	4 237.65	4 298.51	4 302.95
	PSNR/dB	11.79	14.25	16.02	10.95	14.90	15.43	13.68	15.81	17.76
	最佳阈值	(89, 149) (193, 195)	(79, 81) (124, 152) (181, 182)	(71, 73) (116, 121) (141, 148) (183, 186)	(78, 78) (106, 156)	(78, 79) (105, 123) (161, 161)	(75, 76) (101, 102) (110, 122) (157, 158)	(93, 141) (184, 185)	(91, 91) (100, 135) (171, 171)	(31, 36) (86, 88) (114, 127) (169, 175)
L-SHADE	距离测度	4 862.06	4 958.56	4 979.32	6 775.94	6 823.60	6 824.28	4 242.17	4 308.93	4 346.97
	PSNR/dB	12.93	15.17	16.23	10.97	15.01	15.62	13.78	14.73	18.28
	最佳阈值	(86, 148) (193, 193)	(83, 83) (118, 150) (188, 189)	(81, 81) (118, 124) (139, 148) (194, 194)	(77, 78) (106, 156)	(76, 77) (106, 127) (169, 169)	(74, 75) (93, 95) (111, 128) (170, 171)	(91, 135) (180, 180)	(36, 41) (93, 130) (172, 172)	(87, 89) (105, 126) (158, 159) (170, 170)
CJADE	距离测度	4 863.53	4 977.39	4 999.63	6 775.87	6 820.06	6 828.37	4 244.89	4 314.66	4 352.11
	PSNR/dB	13.93	15.64	16.25	11.00	14.89	15.87	14.10	16.52	18.99
	最佳阈值	(87, 149) (194, 194)	(77, 78) (113, 149) (194, 194)	(78, 80) (117, 139) (156, 156) (199, 199)	(75, 78) (106, 155)	(76, 76) (104, 121) (157, 157)	(75, 78) ((106, 117) ((128, 128) ((161, 162)	(92, 131) (177, 177)	(87, 88) (104, 127) (171, 172)	(81, 81) (105, 124) (138, 142) (170, 170)
WOA	距离测度	4 846.78	4 936.64	4 944.23	6 724.17	6 810.15	6 801.26	4 201.23	4 264.65	4 228.01
	PSNR/dB	12.85	15.15	16.13	7.60	14.85	15.00	9.11	13.72	16.24
	最佳阈值	(85, 157) (192, 193)	(75, 76) (130, 142) (195, 195)	(65, 65) (118, 121) (145, 157) (192, 192)	(75, 155) (255, 255)	(77, 93) (104, 129) (164, 164)	(75, 83) (108, 116) (142, 151) (255, 255)	(76, 76) (97, 158)	(120, 44) (91, 136) (182, 182)	(13, 42) (73, 73) (104, 122) (136, 157)
JADE-GL	距离测度	4 863.53	4 977.70	5 001.16	6 771.43	6 825.41	6 834.79	4 245.88	4 325.61	4 385.06
	PSNR/dB	14.02	16.23	17.90	13.51	15.19	16.15	14.22	17.03	19.49
	最佳阈值	(87, 149) (194, 194)	(79, 79) (114, 149) (194, 194)	(78, 78) (114, 136) (155, 155) (194, 194)	(74, 125) (160, 161)	(76, 78) (108, 129) (170, 170)	(74, 78) (106, 117) (129, 129) (172, 172)	(93, 133) (177, 177)	(84, 84) (106, 131) (166, 166)	(81, 81) (106, 121) (144, 144) (185, 185)

Table 2

Fig.6

References 26

1	WIHARTO , ESTI S , MURDOKO S . The hybrid method of SOM artificial neural network and median thresholding for segmentation of blood vessels in the retina image fundus[J]. International Journal of Fuzzy Logic and Intelligent Systems, 2019, 19 (4): 323- 331. doi: 10.5391/IJFIS.2019.19.4.323
2	GAO B , LI X Q , WOO W L , et al. Physics-based image segmentation using first order statistical properties and genetic algorithm for inductive thermography imaging[J]. IEEE Trans.on Image Processing, 2018, 27 (5): 2160- 2175. doi: 10.1109/TIP.2017.2783627
3	SINGH N, GOYAL S.Determination and segmentation of brain tumor using threshold segmentation with morphological operations[M].Soft Computing: Theories and Applications.Singapore: Springer, 2018: 715-726.
4	PAUL D, DAW N, ROY N D, et al.An automated dual threshold band-based approach for malaria parasite segmentation from thick blood smear[M].Emerging Technology in Modelling and Graphics.Singapore: Springer, 2020: 485-500.
5	PARE S , BHANDARI A K , KUMAR A , et al. Backtracking search algorithm for color image multilevel thresholding[J]. Signal Image Video Process, 2018, 12 (2): 385- 392. doi: 10.1007/s11760-017-1170-z
6	OTSU N . A threshold selection method from gray-level histograms[J]. IEEE trans.on Systems, Man, and Cybernetics, 1979, 9 (1): 62- 66. doi: 10.1109/TSMC.1979.4310076
7	QIN J , SHEN X J , MEI F , et al. An Otsu multi-thresholds segmentation algorithm based on improved ACO[J]. The Journal of Supercomputing, 2019, 75 (2): 955- 967. doi: 10.1007/s11227-018-2622-0
8	TRUONG M T N , KIM S . Automatic image thresholding using Otsu's method and entropy weighting scheme for surface defect detection[J]. Soft Computing, 2017, 22 (13): 4197- 4203.
9	PUN T . A new method for grey-level picture thresholding using the entropy of the histogram[J]. Signal Processing, 1980, 2 (3): 223- 237. doi: 10.1016/0165-1684(80)90020-1
10	MISHRA S , PANDA M . Bat algorithm for multilevel colour image segmentation using entropy-based thresholding[J]. Arabian Journal for Science & Engineering, 2018, 43 (6): 7285- 7314.
11	刘健庄, 栗文青. 灰度图象的二维Otsu自动阈值分割法[J]. 自动化学报, 1993, 19 (1): 101- 105.
	LIU J Z , LI W Q . Two-dimensional Otsu automatic threshold segmentation method for gray image[J]. Acta Automatica Sinica, 1993, 19 (1): 101- 105.
12	宋文青, 王英华, 卢红喜, 等. 基于幂次变换的SAR图像Otsu分割法[J]. 系统工程与电子技术, 2015, 37 (7): 1504- 1511.
	SONG W Q , WANG Y H , LU H X , et al. Otsu segmentation algorithm for SAR images based on power transformation[J]. Systems Engineering and Electronics, 2015, 37 (7): 1504- 1511.
13	高飞彪, 陈南南. 二维Otsu图像快速分割方法的改进[J]. 黑河学院学报, 2019, 10 (10): 216- 220.
	GAO F B , CHENG N N . Improvement of 2D Otsu image fast segmentation method[J]. Journal of Heihe University, 2019, 10 (10): 216- 220.
14	MAL S, KUMAR A.Heuristic approach for finding threshold value in image segmentation[M].Emerging Technology in Modelling and Graphics.Singapore: Springer, 2020: 45-53.
15	胡加鑫, 贾鹤鸣, 邢致恺. 基于鲸鱼算法的森林火灾图像多阈值分割[J]. 森林工程, 2018, 34 (4): 70- 74, 95.
	HU J X , JIA H M , XING Z K . Multi-threshold segmentation of forest fire image based on whale algorithm[J]. Forest Engineering, 2018, 34 (4): 70- 74, 95.
16	赵汝海, 孙凡, 朱广. 基于模糊OTSU与布谷鸟寻优的火灾图像多阈值分割算法[J]. 安徽建筑大学学报, 2019, 27 (4): 65- 70, 97.
	ZHAO R H , SUN F , ZHU G . Multi-threshold segmentation algorithm for fire images based on fuzzy OTSU and cuckoo search[J]. Journal of Anhui University of Architecture, 2019, 27 (4): 65- 70, 97.
17	SATAPATHY S C , SRI M R N , RAJINIKANTH V , et al. Multi-level image thresholding using Otsu and chaotic bat algorithm[J]. Neural Computing and Applications, 2018, 29 (12): 1285- 1307. doi: 10.1007/s00521-016-2645-5
18	ZHANG J Q , SANDERSON A C . JADE:adaptive differential evolution with optional external archive[J]. IEEE Trans.on Evolutionary Computation, 2009, 13 (5): 945- 958. doi: 10.1109/TEVC.2009.2014613
19	TANABE R, FUKUNAGA A S.Improving the search perfor-mance of SHADE using linear population size reduction[C]//Proc.of the IEEE Congress on Evolutionary Computation, 2014: 1658-1665.
20	罗钧, 杨永松, 侍宝玉. 基于改进的自适应差分演化算法的二维Otsu多阈值图像分割[J]. 电子与信息学报, 2019, 41 (8): 2017- 2024.
	LUO J , YANG Y S , SHI B Y . Two-dimensional Otsu multi-threshold image segmentation based on improved adaptive differential evolution algorithm[J]. Journal of Electronics & Information Technology, 2019, 41 (8): 2017- 2024.
21	STORN R , PRICE K . Differential evolution-a simple and efficient heuristic for global optimization over continuous spaces[J]. Journal of Global Optimization, 1997, 11 (4): 341- 359. doi: 10.1023/A:1008202821328
22	LI H W , LIU J Y , CHEN L , et al. Chaos-enhanced moth-flame optimization algorithm for global optimization[J]. Journal of Systems Engineering and Electronics, 2019, 30 (6): 1144- 1159. doi: 10.21629/JSEE.2019.06.10
23	WANG X Y , LI J , WANG T B , et al. Satellite constellation design with genetic algorithms based on system performance[J]. Journal of Systems Engineering and Electronics, 2016, 27 (2): 105- 111.
24	TRIVEDI A, SANYAL K, VERMA P, et al.A unified differential evolution algorithm for constrained optimization problems[C]//Proc.of the IEEE Congress on Evolutionary Computation, 2017: 1231-1238.
25	LUO J , WANG Q . A method for axis straightness error evaluation based on improved artificial bee colony algorithm[J]. The International Journal of Advanced Manufacturing Technology, 2014, 71 (5/8): 1501- 1509.
26	ELSHAZLY E , ABDELWAHAB S , ABOUZAID R , et al. A secure image steganography algorithm based on least significant bit and integer wavelet transform[J]. Journal of Systems Engineering and Electronics, 2018, 29 (3): 639- 649. doi: 10.21629/JSEE.2018.03.21

编号	特征	函数表达式	上下界	维度
F₁	US	$f_{1}(x)=\sum_{i=1}^{n} x_{i}^{2}$	[-100, 100]	30
F₂	UN	$f_{2}(x)=\sum\limits_{i=1}^{n-1}\left[100\left(x_{i+1}-x_{i}^{2}\right)+\left(x_{i}-1\right)^{2}\right]$	[-30, 30]	30
F₃	MS	$\begin{array}{*{20}{c}}{f_{3}(x)=0.1\left\{\sin ^{2}\left(3 \pi x_{1}\right)+\sum\limits_{i=1}^{n}\left(x_{i}-1\right)^{2}\left[1+\sin ^{2}\left(3 \pi x_{i}+1\right)\right]+\right.}\\{\left.\left(x_{n}-1\right)^{2}\left[1+\sin ^{2}\left(2 \pi x_{n}\right)\right]\right\}+\sum\limits_{i=1}^{n} u\left(x_{i}, 5, 100, 4\right)}\end{array}$	[-50, 50]	30
F₄	MN	$f_{4}(x)=-\sum\limits_{i=1}^{n} \sin x_{i} \sin ^{20} \frac{i x_{i}^{2}}{\pi}$	[0, π]	10

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Multi-threshold image segmentation of 2D Otsu based on neighborhood search JADE

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