Image Segmentation Algorithm Combining Visual Salient Regions and Active Contour

doi:10.3778/j.issn.1673-9418.2011043

Journal of Frontiers of Computer Science and Technology ›› 2022, Vol. 16 ›› Issue (5): 1155-1168.DOI: 10.3778/j.issn.1673-9418.2011043

• Graphics and Image • Previous Articles Next Articles

Image Segmentation Algorithm Combining Visual Salient Regions and Active Contour

HE Yaru¹^,², GE Hongwei¹^,²^,⁺()

1. Jiangsu Provincial Engineering Laboratory of Pattern Recognition and Computational Intelligence, Jiangnan Univer-sity, Wuxi, Jiangsu 214122, China
2. School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi, Jiangsu 214122, China

Received:2020-11-13 Revised:2021-01-20 Online:2022-05-01 Published:2022-05-19
About author:HE Yaru, born in 1994, M.S. candidate, student member of CCF. Her research interest is computer vision.
GE Hongwei, born in 1967, Ph.D., professor, Ph.D. supervisor. His research interests include artificial intelligence and pattern recognition, image and video processing and analysis, etc.
Supported by:
Innovation Program for Graduate of Jiangsu Province(KYLX16_0781);Priority Academic Development Program of Jiangsu Higher Education Institutions

视觉显著区域和主动轮廓结合的图像分割算法

何亚茹¹^,², 葛洪伟¹^,²^,⁺()

1.江南大学江苏省模式识别与计算机智能工程实验室,江苏无锡 214122
2.江南大学人工智能与计算机学院,江苏无锡 214122

通讯作者: + E-mail: ghw8601@163.com
作者简介:何亚茹（1994—）,女,河北石家庄人,硕士研究生,CCF学生会员,主要研究方向为计算机视觉。
葛洪伟（1967—）,男,江苏无锡人,博士,教授,博士生导师,主要研究方向为人工智能与模式识别、图像及视频的处理与分析等。
基金资助:
江苏省研究生创新计划项目(KYLX16_0781);江苏高校优势学科建设工程资助项目

Abstract

Abstract:

When the traditional regional active contour model is used to segment the weak edge image, the evolution curve is subject to background interference, and it is easy to fall into the local extreme value, which leads to slow evolution speed. Moreover, as the local term only considers the spatial information, it cannot better retain the target boundary, which affects the segmentation accuracy. To solve the above problems, firstly, this paper uses the improved saliency detection algorithm to preprocess the original image, obtains the target candidate regions and automatically sets the initial contour curve. In addition, the obtained priori information of the target is combined with the bitmap with the maximum contrast in the image to be segmented. An adaptive symbolic function is designed to weight the optimized LoG (Laplacian of Gaussian) energy terms, in a linear fashion into RSF (region-scalable fitting) model, improving the adaptive ability of the model. Secondly, a new local grayscale measure is proposed, which is combined with local kernel function to improve the local energy term. It can improve the sensitivity of the model at the weak edge, and accurately locate the target boundary. Experimental results show that this model can automatically set the initial contour and effectively retain the target edge details. Visual and quantitative experimental results show that this model is superior to some mainstream active contour models.

Key words: visual saliency detection, adaptive symbolic function, local gray measure, active contour model, image segmentation

摘要：

传统区域主动轮廓模型在分割弱边缘图像时,演化曲线受背景干扰,易陷入局部极值导致演化速度缓慢;且由于局部项仅考虑空间信息,无法更好保留目标边界,影响分割精度。针对上述问题,首先利用改进的显著性检测方法,对待分割图像进行预处理操作,获取目标候选区域,自动设置初始化轮廓曲线,并将获取的目标先验信息与待分割图像中具有最大对比度的位图相结合,设计自适应符号函数,对优化LoG能量项进行加权,以线性方式融合到RSF模型中,增强模型自适应能力;其次设计新的局部灰度测度,与局部核函数相结合,改进局部能量项,提高模型在弱边缘处的敏感程度,准确定位目标边界。实验结果表明,该模型能够自动设置初始化轮廓,并有效保留目标边缘细节,视觉及定量实验结果证明了该模型优于目前一些主流的主动轮廓模型。

关键词: 视觉显著性检测, 自适应符号函数, 局部灰度测度, 主动轮廓模型, 图像分割

CLC Number:

TP391.41

HE Yaru, GE Hongwei. Image Segmentation Algorithm Combining Visual Salient Regions and Active Contour[J]. Journal of Frontiers of Computer Science and Technology, 2022, 16(5): 1155-1168.

何亚茹, 葛洪伟. 视觉显著区域和主动轮廓结合的图像分割算法[J]. 计算机科学与探索, 2022, 16(5): 1155-1168.

Figures/Tables 13

Fig.1 Effect of contrast measure before and after improvement

Fig.2 Initial contour obtained by different clustering algorithms

Fig.3 Seeds of target

Fig.4 Effectiveness of RBHM model

Fig.5 Flow chart of proposed model

Table 1 Value of v, v 1 to v 5 in each model segmenting sample images on Fig.6 and Fig.7

Image	$v 1$		$v 2 (255 × 255)$		$v 3 (255 × 255)$		$v 4 (255 × 255)$		$v 5 (255 × 255)$		$v (255 × 255)$
Image	Fig.6	Fig.7	Fig.6	Fig.7	Fig.6	Fig.7	Fig.6	Fig.7	Fig.6	Fig.7	Fig.6	Fig.7
$I 1$	3 000.000 0	3 000.000 0	0.002 0	0.001 0	0.000 9	0.001 1	0.001 0	0.001 0	0.005 9	0.005 9	0.001 2	0.000 8
$I 2$	590.000 0	3 000.000 0	0.001 2	0.001 1	0.001 3	0.001 3	0.001 0	0.001 0	0.005 6	0.005 3	0.000 8	0.000 7
$I 3$	4.000 0	4.200 0	0.002 1	0.004 4	0.002 6	0.004 5	0.002 0	0.003 5	0.011 0	0.012 0	0.001 9	0.004 7
$I 4$	1.500 0	5.500 0	0.001 7	0.001 6	0.002 7	0.001 9	0.003 5	0.003 6	0.008 7	0.008 6	0.002 6	0.002 8
$I 5$	1.200 0	15.000 0	0.180 0	0.012 0	0.055 0	0.007 6	0.035 0	0.013 0	0.120 0	0.120 0	0.021 0	0.011 0
$I 6$	4.100 0	0.500 0	0.110 0	0.160 0	0.180 0	0.099 0	0.140 0	0.140 0	0.290 0	0.370 0	0.021 0	0.021 0

Table 1 Value of v, v 1 to v 5 in each model segmenting sample images on Fig.6 and Fig.7

Image	$v 1$		$v 2 (255 × 255)$		$v 3 (255 × 255)$		$v 4 (255 × 255)$		$v 5 (255 × 255)$		$v (255 × 255)$
Image	Fig.6	Fig.7	Fig.6	Fig.7	Fig.6	Fig.7	Fig.6	Fig.7	Fig.6	Fig.7	Fig.6	Fig.7
$I 1$	3 000.000 0	3 000.000 0	0.002 0	0.001 0	0.000 9	0.001 1	0.001 0	0.001 0	0.005 9	0.005 9	0.001 2	0.000 8
$I 2$	590.000 0	3 000.000 0	0.001 2	0.001 1	0.001 3	0.001 3	0.001 0	0.001 0	0.005 6	0.005 3	0.000 8	0.000 7
$I 3$	4.000 0	4.200 0	0.002 1	0.004 4	0.002 6	0.004 5	0.002 0	0.003 5	0.011 0	0.012 0	0.001 9	0.004 7
$I 4$	1.500 0	5.500 0	0.001 7	0.001 6	0.002 7	0.001 9	0.003 5	0.003 6	0.008 7	0.008 6	0.002 6	0.002 8
$I 5$	1.200 0	15.000 0	0.180 0	0.012 0	0.055 0	0.007 6	0.035 0	0.013 0	0.120 0	0.120 0	0.021 0	0.011 0
$I 6$	4.100 0	0.500 0	0.110 0	0.160 0	0.180 0	0.099 0	0.140 0	0.140 0	0.290 0	0.370 0	0.021 0	0.021 0

Fig.6 Segmentation results of manual initialization

图7 Segmentation results of automatic initialization

Table 2 Iteration time required for six models using different initialized contours s

Image	SPF	SPF*	RSF	RSF*	LGIF	LGIF*	ACML	ACML*	LPF	LPF*	RBHM	Ours
$I 1$	3.7	6.2	14.1	3.7	8.4	4.4	4.4	3.9	1.6	1.4	26.6	6.8
$I 2$	5.5	2.3	3.1	1.8	13.0	1.9	3.1	2.2	1.1	1.1	5.8	2.1
$I 3$	4.8	2.2	6.3	1.4	6.3	2.2	7.2	0.8	8.6	1.3	7.2	0.7
$I 4$	13.7	5.3	20.1	8.7	60.1	9.0	24.3	12.6	16.5	11.8	11.7	19.2
$I 5$	25.4	2.6	251.3	2.2	207.6	2.4	215.9	0.8	71.1	13.5	226.4	1.4
$I 6$	10.2	2.5	54.0	29.0	65.6	29.2	47.8	30.1	13.7	13.7	41.4	24.1
均值	10.6	3.5	58.2	7.8	60.2	8.2	50.5	8.4	18.8	7.1	53.2	9.1

Table 2 Iteration time required for six models using different initialized contours s

Image	SPF	SPF*	RSF	RSF*	LGIF	LGIF*	ACML	ACML*	LPF	LPF*	RBHM	Ours
$I 1$	3.7	6.2	14.1	3.7	8.4	4.4	4.4	3.9	1.6	1.4	26.6	6.8
$I 2$	5.5	2.3	3.1	1.8	13.0	1.9	3.1	2.2	1.1	1.1	5.8	2.1
$I 3$	4.8	2.2	6.3	1.4	6.3	2.2	7.2	0.8	8.6	1.3	7.2	0.7
$I 4$	13.7	5.3	20.1	8.7	60.1	9.0	24.3	12.6	16.5	11.8	11.7	19.2
$I 5$	25.4	2.6	251.3	2.2	207.6	2.4	215.9	0.8	71.1	13.5	226.4	1.4
$I 6$	10.2	2.5	54.0	29.0	65.6	29.2	47.8	30.1	13.7	13.7	41.4	24.1
均值	10.6	3.5	58.2	7.8	60.2	8.2	50.5	8.4	18.8	7.1	53.2	9.1

Table 3 Value of v, v 1 to v 5 in each model segmenting sample images on Fig.8

Image	$v 1$	$v 2 (255 × 255)$	$v 3 (255 × 255)$	$v 4 (255 × 255)$	$v 5 (255 × 255)$	$v (255 × 255)$
A	3 000.000 0	0.019 0	0.014 0	0.029 0	0.057 0	0.002 9
B	1.600 0	0.000 3	0.000 4	0.000 5	0.000 5	0.000 4
C	190.000 0	0.000 4	0.003 1	0.001 4	0.005 5	0.002 6
D	0.500 0	0.002 3	0.000 7	0.008 8	0.009 1	0.003 1
E	0.800 0	0.028 0	0.033 0	0.045 0	0.050 0	0.012 0
F	7.700 0	0.320 0	0.160 0	0.400 0	1.000 0	0.120 0
G	2.500 0	0.220 0	0.280 0	0.210 0	0.140 0	0.089 0
H	1.900 0	0.008 4	0.014 0	0.005 4	0.016 0	0.001 6
I	3.000 0	0.064 0	0.091 0	0.086 0	0.200 0	0.004 9
J	31.000 0	0.045 0	0.044 0	0.021 0	0.090 0	0.001 6

Table 3 Value of v, v 1 to v 5 in each model segmenting sample images on Fig.8

Image	$v 1$	$v 2 (255 × 255)$	$v 3 (255 × 255)$	$v 4 (255 × 255)$	$v 5 (255 × 255)$	$v (255 × 255)$
A	3 000.000 0	0.019 0	0.014 0	0.029 0	0.057 0	0.002 9
B	1.600 0	0.000 3	0.000 4	0.000 5	0.000 5	0.000 4
C	190.000 0	0.000 4	0.003 1	0.001 4	0.005 5	0.002 6
D	0.500 0	0.002 3	0.000 7	0.008 8	0.009 1	0.003 1
E	0.800 0	0.028 0	0.033 0	0.045 0	0.050 0	0.012 0
F	7.700 0	0.320 0	0.160 0	0.400 0	1.000 0	0.120 0
G	2.500 0	0.220 0	0.280 0	0.210 0	0.140 0	0.089 0
H	1.900 0	0.008 4	0.014 0	0.005 4	0.016 0	0.001 6
I	3.000 0	0.064 0	0.091 0	0.086 0	0.200 0	0.004 9
J	31.000 0	0.045 0	0.044 0	0.021 0	0.090 0	0.001 6

Fig.8 Segmentation results of six models

Table 4 Segmentation accuracy of six models on Fig.8

Image	SPF*		RSF*		LGIF*		ACML*		LPF*		Ours
Image	DSC	JSI	DSC	JSI	DSC	JSI	DSC	JSI	DSC	JSI	DSC	JSI
A	0.893 7	0.807 9	0.912 2	0.838 5	0.913 4	0.840 6	0.912 1	0.838 3	0.911 3	0.837 0	0.926 8	0.863 6
B	0.850 9	0.740 5	0.889 3	0.800 6	0.891 4	0.804 0	0.894 3	0.808 9	0.894 4	0.808 9	0.905 9	0.828 0
C	0.889 2	0.800 4	0.945 5	0.896 7	0.945 1	0.896 0	0.942 9	0.892 0	0.942 7	0.891 6	0.959 7	0.922 5
D	0.901 9	0.821 4	0.905 6	0.827 4	0.904 9	0.826 3	0.905 2	0.826 9	0.905 9	0.828 0	0.925 2	0.860 9
E	0.924 5	0.859 6	0.928 0	0.865 6	0.929 1	0.867 6	0.928 4	0.866 4	0.926 6	0.863 2	0.937 5	0.882 4
F	0.926 7	0.863 4	0.935 7	0.879 2	0.934 0	0.876 2	0.935 1	0.878 1	0.942 5	0.891 2	0.967 1	0.936 3
G	0.859 2	0.753 2	0.889 7	0.801 3	0.887 3	0.797 4	0.889 7	0.801 3	0.267 3	0.154 3	0.934 6	0.877 2
H	0.833 5	0.714 5	0.870 0	0.769 9	0.860 5	0.755 1	0.900 4	0.818 8	0.868 6	0.767 8	0.912 5	0.839 1
I	0.925 2	0.860 8	0.938 4	0.883 9	0.935 4	0.878 6	0.938 0	0.883 3	0.915 1	0.843 5	0.943 4	0.892 8
J	0.869 2	0.768 7	0.863 7	0.760 2	0.869 8	0.769 6	0.892 9	0.806 5	0.603 4	0.432 0	0.921 0	0.853 6
均值	0.887 4	0.799 0	0.907 8	0.832 3	0.907 1	0.831 1	0.913 9	0.842 1	0.817 8	0.731 8	0.933 4	0.875 6

Table 5 Segmentation evaluation of six models on Fig.8 (HD)

Image	SPF*	RSF*	LGIF*	ACML*	LPF*	Ours
A	19.65	18.97	18.00	18.97	19.42	9.85
B	25.94	14.00	14.00	14.00	4.00	13.00
C	22.56	6.40	6.40	19.03	19.03	5.39
D	10.63	6.32	6.32	6.32	6.32	6.32
E	18.44	8.25	7.81	7.81	8.25	2.83
F	26.00	26.25	26.25	26.25	26.25	5.00
G	10.77	11.18	11.18	11.18	173.59	8.94
H	84.01	83.73	30.15	9.06	30.15	9.22
I	7.00	23.02	34.54	8.94	16.03	5.10
J	60.21	80.31	80.31	85.62	85.38	10.00
均值	28.52	27.84	23.50	20.72	38.84	7.57

References 28

[1]	HAN B, WU Y. Active contours driven by global and local weighted signed pressure force for image segmentation[J]. Pattern Recognition, 2019, 88: 715-728. DOI URL
[2]	SOOMRO S, MUNIR A, CHOI K N, et al. Fuzzy C-means clustering based active contour model driven by edge scaled region information[J]. Expert Systems with Applications, 2019, 120: 387-396. DOI URL
[3]	KASS M, WITKIN A, TERZOPOULOS D, et al. Snakes: active contour models[J]. International Journal of Computer Vision, 1988, 1(4): 321-331. DOI URL
[4]	GAO S B, YANG J. Saliency-seeded localizing region-based active contour for automatic natural object segmentation[C]// Proceedings of the 21st International Conference on Pattern Recognition, Tsukuba, Nov 11-15, 2012. Washington: IEEE Computer Society, 2012: 3644-3647.
[5]	CASELLES V, CATTE F, COLL T, et al. A geometric model for active contours in image processing[J]. Numerische Mathe-matik, 1993, 66(1): 1-31.
[6]	CHAN T F, VESE L A. Active contours without edges[J]. IEEE Transactions on Image Processing, 2001, 10(2): 266-277. DOI URL
[7]	LI C M, KAO C Y, GORE J C, et al. Minimization of region-scalable fitting energy for image segmentation[J]. IEEE Trans-actions on Image Processing, 2008, 17(10): 1940-1949.
[8]	DING K, XIAO L F, WENG G R. Active contours driven by local prefitting energy for fast image segmentation[J]. Pattern Recognition Letters, 2018, 104: 29-36. DOI URL
[9]	ZHANG K H, ZHANG L, SONG H H, et al. Active contours with selective local or global segmentation: a new formulation and level set method[J]. Image & Vision Computing, 2010, 28(4): 668-676.
[10]	WANG L, LI C M, SUN Q S, et al. Active contours driven by local and global intensity fitting energy with application to brain MR image segmentation[J]. Computerized Medical Imaging & Graphics, 2009, 33(7): 520-531.
[11]	赵杰, 祁永梅, 潘正勇. 融合模糊全局和双核局部信息的活动轮廓模型[J]. 小型微型计算机系统, 2014, 35(3): 663-666.
	ZHAO J, QI Y M, PAN Z Y. Active contour model of combining fuzzy global and dual-core local information[J]. Journal of Chinese Computer Systems, 2014, 35(3): 663-666.
[12]	DING K Y, XIAO L F, WENG G R. Active contours driven by region-scalable fitting and optimized Laplacian of Gaussian energy for image segmentation[J]. Signal Processing, 2017, 134: 224-233. DOI URL
[13]	陶永鹏, 景雨, 顼聪. 随机森林和水平集的椎骨CT分割方法[J]. 小型微型计算机系统, 2019, 40(6): 1336-1339.
	TAO Y P, JING Y, XU C. Ivertebra CT segmentation method based on random forest and Chan Vese[J]. Journal of Chinese Computer Systems, 2019, 40(6): 1336-1339.
[14]	LI B N, CHUI C K, CHANG S, et al. Integrating spatial fuzzy clustering with level set methods for automated medical image segmentation[J]. Computers in Biology & Medicine, 2011, 41(1): 1-10.
[15]	YANG Y Y, WANG R, FENG C. Level set formulation for automatic medical image segmentation based on fuzzy clus-tering[J]. Signal Processing Image Communication, 2020, 87: 115907. DOI URL
[16]	FU H Z, CAO X C, ZHUO W T. Cluster-based co-saliency detection[J]. IEEE Transactions on Image Processing, 2013, 22(10): 3766-3778. DOI URL
[17]	YUAN Y, LI C, KIM J, et al. Reversion correction and regu-larized random walk ranking for saliency detection[J]. IEEE Transactions on Image Processing, 2018, 27(3): 1311-1322. DOI URL
[18]	林喜兰, 陈秀宏, 肖林云. 局部信息熵的快速混合测地区域活动轮廓模型[J]. 计算机科学与探索, 2016, 10(9): 1332-1340.
	LIN X L, CHEN X H, XIAO L Y. Fast hybrid geodesic region active contour model of local entropy[J]. Journal of Frontiers of Computer Science and Technology, 2016, 10(9): 1332-1340.
[19]	LI C M, XU C Y, GUI C F, et al. Distance regularized level set evolution and its application to image segmentation[J]. IEEE Transactions on Image Processing, 2010, 19(12): 3243-3254. DOI URL
[20]	KIMMEL R, BRUCKSTEIN A M. Regularized Laplacian zero crossings as optimal edge integrators[J]. International Journal of Computer Vision, 2003, 53(3): 225-243. DOI URL
[21]	ZHOU L, YANG Z, ZHOU Z, et al. Salient region detection using diffusion process on a two-layer sparse graph[J]. IEEE Transactions on Image Processing, 2017, 26(12): 5882-5894. DOI URL
[22]	ZONG J J, QIUTS, LI W D, et al. Automatic ultrasound image segmentation based on local entropy and active contour model[J]. Computers & Mathematics with Applications, 2019, 78: 929-943. DOI URL
[23]	CHENG Y. Mean shift, mode seeking and clustering[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1995, 17(8): 790-799. DOI URL
[24]	FU Y, CHENG J, LI Z L, et al. Saliency cuts: an automatic approach to object segmentation[C]// Proceedings of the 19th International Conference on Pattern Recognition, Tampa, Dec 8-11, 2008. Washington: IEEE Computer Society, 2008: 1-4.
[25]	BAI X F, WANG W J. Saliency-SVM: an automatic approach for image segmentation[J]. Neurocomputing, 2014, 136(20): 243-255. DOI URL
[26]	李社蕾, 黄梦醒. 改进几何活动轮廓模型的水下图像分割算法研究[J]. 小型微型计算机系统, 2019, 40(3): 671-675.
	LI S L, HUANG M X. Research of underwater image segmentation algorithm based on the improved geometric active contour models[J]. Journal of Chinese Computer Systems, 2019, 40(3): 671-675.
[27]	ALPERT S, GALUN M, BRANDT A, et al. Image segmenta-tion by probabilistic bottom-up aggregation and cue integration[C]// Proceedings of the 2007 IEEE Computer Society Confer-ence on Computer Vision and Pattern Recognition, Minnea-polis, Jun 18-23, 2007. Washington: IEEE Computer Society, 2007: 1-8.
[28]	温立民, 巨永锋, 闫茂德. 基于自然统计特征分布的交通图像雾浓度检测[J]. 电子学报, 2017, 45(8): 1888-1895. DOI
	WEN L M, JU Y F, YAN M D. Inspection of fog density for traffic image based on distribution characteristics of natural statistics[J]. Acta Electronica Sinica, 2017, 45(8): 1888-1895.

Image Segmentation Algorithm Combining Visual Salient Regions and Active Contour

视觉显著区域和主动轮廓结合的图像分割算法

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[7]	HAN Chenghui, WANG Huiqin, HU Yan. Enhancement and Segmentation Algorithm Study for Low Contrast Fire Image [J]. Journal of Frontiers of Computer Science and Technology, 2018, 12(1): 163-170.
[8]	ZHANG Na, WANG Huiqin, HU Yan. Smoke Image Segmentation Algorithm Based on Rough Set and Region Growing [J]. Journal of Frontiers of Computer Science and Technology, 2017, 11(8): 1296-1304.
[9]	LIN Xilan, CHEN Xiuhong, XIAO Linyun. Fast Hybrid Geodesic Region Active Contour Model of Local Entropy [J]. Journal of Frontiers of Computer Science and Technology, 2016, 10(9): 1332-1340.
[10]	YAO Longyang, ZHANG Qinghua, HU Shuaipeng1, ZHANG Qiang. Rough Entropy for Image Segmentation Based on Approximation Sets and Particle Swarm Optimization [J]. Journal of Frontiers of Computer Science and Technology, 2016, 10(5): 699-708.
[11]	ZHANG Xinming, TU Qiang, YIN Xinxin. Efficient BBO Algorithm Based on Hybrid Migration and Its Application to Image Segmentation [J]. Journal of Frontiers of Computer Science and Technology, 2016, 10(10): 1459-1468.
[12]	HE Jianfeng, FU Zeng, YI Sanli, XIANG Yan, CUI Rui. Image Segmentation with Multi-Threshold of Gray-Level & Gradient-Magnitude Entropy Based on Genetic Algorithm [J]. Journal of Frontiers of Computer Science and Technology, 2015, 9(8): 995-1003.
[13]	CAI Qiang, LIU Yaqi, CAO Jian, MAO Dianhui, LI Haisheng. Interactive Image Segmentation Algorithm Based on SLIC and Delaunay Graph Cut [J]. Journal of Frontiers of Computer Science and Technology, 2015, 9(4): 482-490.
[14]	LIU Liqun, HUO Jiuyuan, WANG Lianguo, HAN Junying. Harmony Search Algorithm Based on Shuffled Frog Leaping and Bacterial Foraging and Its Application in Image [J]. Journal of Frontiers of Computer Science and Technology, 2015, 9(1): 119-128.
[15]	BAI Xuefei, WANG Wenjian. Chan-Vese Model with Adaptive Initial Contour for Image Segmentation [J]. Journal of Frontiers of Computer Science and Technology, 2013, 7(12): 1115-1124.