圆形直方图线性化的高精度高适应性多阈值分割方法

doi:10.19678/j.issn.1000-3428.0066751

摘要/Abstract

摘要：

针对已有彩色图像多阈值分割方法存在的分割精度不高、分割适应性较差等问题，提出一种圆形直方图线性化的高精度高适应性多阈值分割方法。在对输入彩色图像进行超像素预处理后，构建累积分布方差最大化准则，将圆形直方图截断后延展为线性直方图。在线性直方图上，结合Tsallis熵和类间方差构建一个新的多阈值分割目标函数。引入麻雀搜索算法，快速求解多阈值分割目标函数得到最优阈值进行阈值分割。在8幅合成图像和500幅真实世界图像上将提出方法和9种不同的彩色图像分割方法进行全面比较，在峰值信噪比（PSNR）、结构相似性（SSIM）、特征相似度（FSIM）、概率兰德指数、全局一致性误差（GCE）、信息差异6个量化评价指标上的综合实验结果表明，提出方法在计算效率方面与比较方法大致持平，但在分割精度和分割适应性方面明显优于比较方法，在PSNR、SSIM、FSIM和GCE等评价指标上分别以19.95 dB、0.80、0.94和0.16取得最优结果。

关键词: 多阈值分割, 圆形直方图, 累积分布方差, 自适应Tsallis熵, 麻雀搜索算法

Abstract:

To address the problems of low segmentation accuracy and poor adaptability of existing multi-threshold segmentation methods for color images, a multi-threshold segmentation method with high accuracy and adaptability based on circular histogram linearization is proposed. After superpixel preprocessing of the input color image, the method first constructs the cumulative distribution variance maximization criterion, based on which the circular histogram is truncated and extended into a linear histogram.Thereafter, a new multi-threshold segmentation objective function is constructed by combining the between-class variance and Tsallis entropy on the linear histogram. Finally, the Sparrow Search Algorithm(SSA) is introduced to quickly solve the multi-threshold segmentation objective function to obtain the optimal threshold.On eight synthetic images and 500 real world images, the proposed method is comprehensively compared with nine different color image segmentation methods.The comprehensive experimental results on six quantitative evaluation indicators, such as Peak Signal-to-Noise Ratio(PSNR), Structural Similarity Index Measure(SSIM), Feature Similarity Index Measure(FSIM), Probabilistic Rand Index(PRI), Global Consistency Error(GSE), and Variation of Information(VI), show that, the proposed method is approximately equal to the compared method in computational efficiency, but it is significantly better than the compared nine methods in segmentation accuracy and adaptability.The proposed method is ranked first in terms of PSNR(19.95 dB), SSIM(0.80), FSIM(0.94), and GSE(0.16).

Key words: multi-threshold segmentation, circular histogram, cumulative distribution variance, adaptive Tsallis entropy, Sparrow Search Algorithm(SSA)

黄聪, 邹耀斌, 孙水发. 圆形直方图线性化的高精度高适应性多阈值分割方法[J]. 计算机工程, 2024, 50(1): 259-270.

Cong HUANG, Yaobin ZOU, Shuifa SUN. Multi-threshold Segmentation Method with High Accuracy and Adaptability Using Circular Histogram Linearization[J]. Computer Engineering, 2024, 50(1): 259-270.

https://www.ecice06.com/CN/Y2024/V50/I1/259

图/表 15

图1 MMGR-WT方法的一般步骤

Fig.1 General steps of the MMGR-WT method

图2 圆形直方图线性化阈值分割方法的基本步骤和关键问题

Fig.2 Basic steps and key problems of circular histogram linearization threshold segmentation method

图3 色调分量与圆形直方图的关系以及错误截断点所得误分割结果图像

Fig.3 Relation between H component and circular histogram and the image segmentation result by a wrong breakpoint

图4 LCL、CDEL和CDVMCL方法在3幅测试图像上所得的线性直方图

Fig.4 Linear histograms obtained by LCL, CDEL and CDVMCL methods on three test images

图5 7种群体智能优化算法在3个基准函数上的收敛曲线、最优精度以及运行时间

Fig.5 Convergence curve, optimal accuracy and elapsed time of seven swarm intelligence optimization algorithms on three benchmark functions

图6 MTCHL方法流程

Fig.6 Procedure of the MTCHL method

图7 10种分割方法在4幅添加不同比例高斯噪声合成图像上的分割结果

Fig.7 Results of ten segmentation methods on four synthetic images with Gaussian noise of different ratios

图8 10种分割方法在4幅添加不同比例椒盐噪声合成图像上的分割结果

Fig.8 Results of ten segmentation methods on four synthetic images with Salt & Pepper noise of different ratios

图9 10种分割方法在BSDS数据集中的9幅代表性真实世界图像上的分割结果

Fig.9 Results of ten segmentation methods on nine representative real world images in the BSDS dataset

图10 10种分割方法在BSDS数据集中的6幅代表性真实世界图像上的分割结果

Fig.10 Results of ten segmentation methods on six representative real world images in the BSDS dataset

图11 10种分割方法在BSDS数据集上的平均CPU运行时间

Fig.11 Average CPU elapsed time of ten segmentation methods on the BSDS dataset

参考文献 31

1	邢致恺, 贾鹤鸣, 宋文龙. 基于莱维飞行樽海鞘群优化算法的多阈值图像分割. 自动化学报, 2021, 47(2): 363- 377.
	XING Z K, JIA H M, SONG W L. Levy flight trajectory-based salp swarm algorithm for multilevel thresholding image segmentation. Acta Automatica Sinica, 2021, 47(2): 363- 377.
2	MA G, YUE X. An improved whale optimization algorithm based on multilevel threshold image segmentation using the Otsu method. Engineering Applications of Artificial Intelligence, 2022, 113, 104960. doi: 10.1016/j.engappai.2022.104960
3	付雪, 朱良宽, 黄建平, 等. 基于改进北方苍鹰优化算法的多阈值图像分割. 计算机工程, 2023, 49(7): 232- 241. URL
	FU X, ZHU L K, HUANG J P, et al. Multi-threshold image segmentation based on improved northern goshawk optimization algorithm. Computer Engineering, 2023, 49(7): 232- 241. URL
4	CHEN Y, WANG M, HEIDARI A A, et al. Multi-threshold image segmentation using a multi-strategy shuffled frog leaping algorithm. Expert Systems with Applications, 2022, 194, 116511. doi: 10.1016/j.eswa.2022.116511
5	聂启颖, 朱振才, 张永合, 等. 面向深空探测图像分割的群智能混合优化算法. 激光与光电子学进展, 2021, 58(2): 0210002.
	NIE Q Y, ZHU Z C, ZHANG Y H, et al. Group intelligent hybrid optimization algorithm for image segmentation of deep space exploration. Laser & Optoelectronics Progress, 2021, 58(2): 0210002.
6	孙振营, 张文. 基于缎蓝园丁鸟算法优化的多阈值遥感图像分割. 国外电子测量技术, 2022, 41(2): 33- 39.
	SUN Z Y, ZHANG W. Multi-threshold remote sensing image segmentation method based on satin bower bird optimization algorithm. Foreign Electronic Measurement Technology, 2022, 41(2): 33- 39.
7	BHANDARI A K. A novel beta differential evolution algorithm-based fast multilevel thresholding for color image segmentation. Neural Computing and Applications, 2020, 32(9): 4583- 4613. doi: 10.1007/s00521-018-3771-z
8	张海涛, 程新文, 熊红伟, 等. 改进蜂群算法的图像阈值分割方法. 计算机应用研究, 2017, 34(12): 3880- 3884.
	ZHANG H T, CHENG X W, XIONG H W, et al. Image threshold segmentation method based on improved artificial bee colony. Application Research of Computers, 2017, 34(12): 3880- 3884.
9	RODRÍGUEZ-ESPARZA E, ZANELLA-CALZADA L A, OLIVA D, et al. An efficient Harris Hawks-inspired image segmentation method. Expert Systems with Applications, 2020, 155, 113428. doi: 10.1016/j.eswa.2020.113428
10	马军, 贾鹤鸣. 基于混沌电磁场优化算法的多阈值彩色图像分割. 计算机应用与软件, 2020, 37(3): 244- 250. doi: 10.3969/j.issn.1000-386x.2020.03.041
	MA J, JIA H M. Multi-threshold color image segmentation based on chaotic electromagnetic field optimization. Computer Applications and Software, 2020, 37(3): 244- 250. doi: 10.3969/j.issn.1000-386x.2020.03.041
11	徐汝利, 黄樟灿, 谢秦秦, 等. 基于金字塔演化策略的彩色图像多阈值分割. 计算机科学, 2022, 49(6): 231- 237.
	XU R L, HUANG Z C, XIE Q Q, et al. Multi-threshold segmentation for color image based on pyramid evolution strategy. Computer Science, 2022, 49(6): 231- 237.
12	KANG C, WU C M, FAN J L. Lorenz curve-based entropy thresholding on circular histogram. IEEE Access, 2020, 8, 17025- 17038. doi: 10.1109/ACCESS.2020.2964335
13	KANG C, WU C M, FAN J L. Entropy-based circular histogram thresholding for color image segmentation. Signal, Image and Video Processing, 2021, 15(1): 129- 138. doi: 10.1007/s11760-020-01723-2
14	张智豪, 范九伦. 基于S分量指数加权H分量的圆形直方图阈值法. 计算机工程, 2022, 48(10): 238- 244. URL
	ZHANG Z H, FAN J L. Circular histogram thresholding method based on S-component exponential weighted H-component. Computer Engineering, 2022, 48(10): 238- 244. URL
15	XUE J K, SHEN B. A novel swarm intelligence optimization approach: sparrow search algorithm. Systems Science & Control Engineering, 2020, 8(1): 22- 34.
16	HU Z W, ZOU Q, LI Q Q. Watershed superpixel[C]//Proceedings of IEEE International Conference on Image Processing. Washington D. C., USA: IEEE Press, 2015: 349-353.
17	LEI T, JIA X H, ZHANG Y N, et al. Superpixel-based fast fuzzy C-means clustering for color image segmentation. IEEE Transactions on Fuzzy Systems, 2018, 27(9): 1753- 1766.
18	ARBELÁEZ P, MAIRE M, FOWLKES C, et al. Contour detection and hierarchical image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2011, 33(5): 898- 916. doi: 10.1109/TPAMI.2010.161
19	KENNEDY J, EBERHART R. Particle swarm optimization[C]//Proceedings of ICNN'95. Washington D. C., USA: IEEE Press, 1995: 1942-1948.
20	DORIGO M, BIRATTARI M, STUTZLE T. Ant colony optimization. IEEE Computational Intelligence Magazine, 2006, 1(4): 28- 39. doi: 10.1109/MCI.2006.329691
21	MIRJALILI S. Moth-flame optimization algorithm: a novel nature-inspired heuristic paradigm. Knowledge-Based Systems, 2015, 89, 228- 249. doi: 10.1016/j.knosys.2015.07.006
22	ABDOLLAHZADEH B, GHAREHCHOPOGH F S, MIRJALILI S. African vultures optimization algorithm: a new nature-inspired metaheuristic algorithm for global optimization problems. Computers & Industrial Engineering, 2021, 158, 107408.
23	DEHGHANI M, HUBÁLOVSKÝ Š, TROJOVSKÝ P. Northern goshawk optimization: a new swarm-based algorithm for solving optimization problems. IEEE Access, 2021, 9, 162059- 162080. doi: 10.1109/ACCESS.2021.3133286
24	BRAIK M, HAMMOURI A, ATWAN J, et al. White shark optimizer: a novel bio-inspired meta-heuristic algorithm for global optimization problems. Knowledge-Based Systems, 2022, 243, 108457. doi: 10.1016/j.knosys.2022.108457
25	TAYMAZ R F, AHAD K A. A hybrid firefly and particle swarm optimization algorithm applied to multilevel image thresholding. Multimedia Systems, 2021, 27(1): 125- 142. doi: 10.1007/s00530-020-00716-y
26	ALOMOUSH W, ALROSAN A, ALOMARI Y M, et al. Fully automatic grayscale image segmentation based fuzzy c-means with firefly mate algorithm. Journal of Ambient Intelligence and Humanized Computing, 2022, 13(9): 4519- 4541. doi: 10.1007/s12652-021-03430-3
27	DHAL K G, DAS A, RAY S, et al. Archimedes optimizer-based fast and robust fuzzy clustering for noisy image segmentation. The Journal of Supercomputing, 2023, 79(4): 3691- 3730. doi: 10.1007/s11227-022-04769-w
28	LEI T, JIA X H, ZHANG Y N, et al. Significantly fast and robust fuzzy c-means clustering algorithm based on morphological reconstruction and membership filtering. IEEE Transactions on Fuzzy Systems, 2018, 26(5): 3027- 3041. doi: 10.1109/TFUZZ.2018.2796074
29	LEI T, LIU P, JIA X H, et al. Automatic fuzzy clustering framework for image segmentation. IEEE Transactions on Fuzzy Systems, 2019, 28(9): 2078- 2092.
30	佟雨兵, 张其善, 祁云平. 基于PSNR与SSIM联合的图像质量评价模型. 中国图象图形学报, 2006, 12, 1758- 1763.
	TONG Y B, ZHANG Q S, QI Y P. Image quality evaluation model based on the combination of PSNR and SSIM. Journal of Image and Graphics, 2006, 12, 1758- 1763.
31	WANG X F, TANG Y X, MASNOU S, et al. A global/local affinity graph for image segmentation. IEEE Transactions on Image Processing, 2015, 24(4): 1399- 1411. doi: 10.1109/TIP.2015.2397313

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