变精度最小平方粗糙熵的图像分割算法
|
摘要
图像处理是获取信息的重要途径且被广泛地应用到军事、医学和交通等重要领域,图像分割在图像处理中占有重要地位。针对图像处理分割过程中的不确定性,为获取更加精确的图像分割效果,提出变精度最小平方粗糙熵和粒子群的图像单阈值分割算法。该单阈值分割算法用变精度粗糙集表示图像,以变精度最小平方粗糙熵求解最佳分割阈值,借助粒子群优化算法提高分割效率。实验表明,该单阈值分割算法明显优于最大平均信息熵法,且说明了变精度粗糙熵能够处理图像分割过程出现的不确定性。
Image processing is an important way to obtain information and is widely used in important fields such as military,medical and transportation fields.Image segmentation plays an important role in image processing.Aiming at the uncertainty in the process of image segmentation,and in order to obtain more accurate image segmentation results,we proposes a single-threshold image segmentation algorithm based on variable precision least square rough entropy and particle swarm optimization.It uses the variable precision rough set to represent the image,utilizes the variable precision least square rough entropy to solve the optimal segmentation threshold,and employs the particle swarm optimization to improve segmentation efficiency.Experimental results show that the single-threshold segmentation algorithm is superior to the maximum average entropy method,and demonstrates that variable precision rough entropy can settle the uncertainty problem in image segmentation process.
Image processing is an important way to obtain information and is widely used in important fields such as military,medical and transportation fields.Image segmentation plays an important role in image processing.Aiming at the uncertainty in the process of image segmentation,and in order to obtain more accurate image segmentation results,we proposes a single-threshold image segmentation algorithm based on variable precision least square rough entropy and particle swarm optimization.It uses the variable precision rough set to represent the image,utilizes the variable precision least square rough entropy to solve the optimal segmentation threshold,and employs the particle swarm optimization to improve segmentation efficiency.Experimental results show that the single-threshold segmentation algorithm is superior to the maximum average entropy method,and demonstrates that variable precision rough entropy can settle the uncertainty problem in image segmentation process.
引文
[1]Yao Long-yang,Zhang Qing-hua,Hu Shuai-peng,et al.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.(in Chinese)
[2]Xu Xin-zheng,Ding Shi-fei,Shi Zhong-zhi,et al.New theories and methods of image segmentation[J].Acta Electronica Sinica,2010,38(Z1):76-82.(in Chinese)
[3]Zhang Qing-hua,Wang Guo-yin,Xiao Yu.Approximation sets of rough sets[J].Journal of Software,2012,23(7):1745-1759.(in Chinese)
[4]Ma Wen-ping,Huang Yuan-yuan,Li Hao,et al.Image segmentation based on rough set and differential immune fuzzy clustering algorithm[J].Journal of Software,2014,25(11):2675-2689.(in Chinese)
[5]Zhang Dong-bo,Wang Yao-nan,Huang Hui-xian.Rough neural network based on variable precision rough set[J].Journal of Electronics&Information Technology,2008,30(8):1913-1917.(in Chinese)
[6]Cui Li-qun,Huang Dian-ping,Song Xiao.Multi-threshold method for image segmentation based on cloud model artificial fish swarm algorithm[J].Computer Engineering and Applications,2017,53(6):204-208.(in Chinese)
[7]Zhang Wen-xiu,Wu Wei-zhi,Liang Ji-ye.Rough set theory and method[M].Beijing:Science Press,2001.(in Chinese)
[8]Li Ting-ting,Jiang Zhao-hui,Rao Yuan,et al.Image segmentation based on gene expression programming and spatial fuzzy clustering[J].Journal of Image and Graphics,2017,22(5):575-583.(in Chinese)
[9]Li Wei-bin,Gao Er,Song Song-he.A global minimization method for image segmentation[J].Journal of Electronics&Information Technology,2013,35(4):791-796.(in Chinese)
[10]Phophalia A,Mitra S K.3D MR image denoising using rough set and kernel PCA method[J].Magnetic Resonance Imaging,2017,36:135-145.
[11]Ji Z,Huang Y,Sun Q,et al.A rough set bounded spatially constrained asymmetric Gaussian mixture model for image segmentation[J].PLoS ONE,2017,12(1):697-708.
[12]Zhang Yong-mei,Ba De-kai,Xing Kuo.A method of fuzzy threshold for adaptive image segmentation[J].Computer Measurement&Control,2016,24(4):126-128.(in Chinese)
[13]Zhang Ying-chun,Guo He.Image segmentation based on rough set and new energy formula[J].Acta Automatica Sinica,2015,41(11):1913-1925.(in Chinese)
[14]Wu Tao.Adaptive rough entropy method for image thresholding[J].Journal of Image and Graphics,2014,19(1):1-10.(in Chinese)
[15]Xu Lu-ping.Digital image processing[M].Beijing:Science Press.2007.(in Chinese)
[16]Dubey Y K,Mushrif M M,Mitra K.Segmentation of brain MR images using rough set based intuitionistic fuzzy clustering[J].Biocybernetics&Biomedical Engineering,2016,36(2):413-426.
[17]Sarkar J P,Saha I,Maulik U.Rough possibilistic type-2fuzzy C-means clustering for MR brain image segmentation[J].Applied Soft Computing,2016,46(C):527-536.
[1]姚龙洋,张清华,胡帅鹏,等.基于近似集与粒子群的粗糙熵图像分割方法[J].计算机科学与探索,2016,10(5):699-708.
[2]许新征,丁世飞,史忠植,等.图像分割的新理论和新方法[J].电子学报,2010,38(Z1):76-82.
[3]张清华,王国胤,肖雨.粗糙集的近似集[J].软件学报,2012,23(7):1745-1759.
[4]马文萍,黄媛媛,李豪,等.基于粗糙集与差分免疫模糊聚类算法的图像分割[J].软件学报,2014,25(11):2675-2689.
[5]张东波,王耀南,黄辉先.基于变精度粗糙集的粗集神经网络[J].电子与信息学报,2008,30(8):1913-1917.
[6]崔丽群,黄殿平,宋晓.基于云模型鱼群算法的多阈值图像分割研究[J].计算机工程与应用,2017,53(6):204-208.
[7]张文修,吴伟志,梁吉业.粗糙集理论与方法[M].北京:科学出版社,2001.
[8]李婷婷,江朝晖,饶元,等.结合基因表达式编程与空间模糊聚类的图像分割[J].中国图象图形学报,2017,22(5):575-583.
[9]李伟斌,高二,宋松和.一种全局最小化的图像分割方法[J].电子与信息学报,2013,35(4):791-796.
[12]张永梅,巴德凯,邢阔.基于模糊阈值的自适应图像分割方法[J].计算机测量与控制,2016,24(4):126-128.
[13]张迎春,郭禾.基于粗糙集和新能量公式的水平集图像分割[J].自动化学报,2015,41(11):1913-1925.
[14]吴涛.图像阈值化的自适应粗糙熵方法[J].中国图象图形学报,2014,19(1):1-10.
[15]许录平.数值图像处理[M].北京:科学出版社,2007.
[2]Xu Xin-zheng,Ding Shi-fei,Shi Zhong-zhi,et al.New theories and methods of image segmentation[J].Acta Electronica Sinica,2010,38(Z1):76-82.(in Chinese)
[3]Zhang Qing-hua,Wang Guo-yin,Xiao Yu.Approximation sets of rough sets[J].Journal of Software,2012,23(7):1745-1759.(in Chinese)
[4]Ma Wen-ping,Huang Yuan-yuan,Li Hao,et al.Image segmentation based on rough set and differential immune fuzzy clustering algorithm[J].Journal of Software,2014,25(11):2675-2689.(in Chinese)
[5]Zhang Dong-bo,Wang Yao-nan,Huang Hui-xian.Rough neural network based on variable precision rough set[J].Journal of Electronics&Information Technology,2008,30(8):1913-1917.(in Chinese)
[6]Cui Li-qun,Huang Dian-ping,Song Xiao.Multi-threshold method for image segmentation based on cloud model artificial fish swarm algorithm[J].Computer Engineering and Applications,2017,53(6):204-208.(in Chinese)
[7]Zhang Wen-xiu,Wu Wei-zhi,Liang Ji-ye.Rough set theory and method[M].Beijing:Science Press,2001.(in Chinese)
[8]Li Ting-ting,Jiang Zhao-hui,Rao Yuan,et al.Image segmentation based on gene expression programming and spatial fuzzy clustering[J].Journal of Image and Graphics,2017,22(5):575-583.(in Chinese)
[9]Li Wei-bin,Gao Er,Song Song-he.A global minimization method for image segmentation[J].Journal of Electronics&Information Technology,2013,35(4):791-796.(in Chinese)
[10]Phophalia A,Mitra S K.3D MR image denoising using rough set and kernel PCA method[J].Magnetic Resonance Imaging,2017,36:135-145.
[11]Ji Z,Huang Y,Sun Q,et al.A rough set bounded spatially constrained asymmetric Gaussian mixture model for image segmentation[J].PLoS ONE,2017,12(1):697-708.
[12]Zhang Yong-mei,Ba De-kai,Xing Kuo.A method of fuzzy threshold for adaptive image segmentation[J].Computer Measurement&Control,2016,24(4):126-128.(in Chinese)
[13]Zhang Ying-chun,Guo He.Image segmentation based on rough set and new energy formula[J].Acta Automatica Sinica,2015,41(11):1913-1925.(in Chinese)
[14]Wu Tao.Adaptive rough entropy method for image thresholding[J].Journal of Image and Graphics,2014,19(1):1-10.(in Chinese)
[15]Xu Lu-ping.Digital image processing[M].Beijing:Science Press.2007.(in Chinese)
[16]Dubey Y K,Mushrif M M,Mitra K.Segmentation of brain MR images using rough set based intuitionistic fuzzy clustering[J].Biocybernetics&Biomedical Engineering,2016,36(2):413-426.
[17]Sarkar J P,Saha I,Maulik U.Rough possibilistic type-2fuzzy C-means clustering for MR brain image segmentation[J].Applied Soft Computing,2016,46(C):527-536.
[1]姚龙洋,张清华,胡帅鹏,等.基于近似集与粒子群的粗糙熵图像分割方法[J].计算机科学与探索,2016,10(5):699-708.
[2]许新征,丁世飞,史忠植,等.图像分割的新理论和新方法[J].电子学报,2010,38(Z1):76-82.
[3]张清华,王国胤,肖雨.粗糙集的近似集[J].软件学报,2012,23(7):1745-1759.
[4]马文萍,黄媛媛,李豪,等.基于粗糙集与差分免疫模糊聚类算法的图像分割[J].软件学报,2014,25(11):2675-2689.
[5]张东波,王耀南,黄辉先.基于变精度粗糙集的粗集神经网络[J].电子与信息学报,2008,30(8):1913-1917.
[6]崔丽群,黄殿平,宋晓.基于云模型鱼群算法的多阈值图像分割研究[J].计算机工程与应用,2017,53(6):204-208.
[7]张文修,吴伟志,梁吉业.粗糙集理论与方法[M].北京:科学出版社,2001.
[8]李婷婷,江朝晖,饶元,等.结合基因表达式编程与空间模糊聚类的图像分割[J].中国图象图形学报,2017,22(5):575-583.
[9]李伟斌,高二,宋松和.一种全局最小化的图像分割方法[J].电子与信息学报,2013,35(4):791-796.
[12]张永梅,巴德凯,邢阔.基于模糊阈值的自适应图像分割方法[J].计算机测量与控制,2016,24(4):126-128.
[13]张迎春,郭禾.基于粗糙集和新能量公式的水平集图像分割[J].自动化学报,2015,41(11):1913-1925.
[14]吴涛.图像阈值化的自适应粗糙熵方法[J].中国图象图形学报,2014,19(1):1-10.
[15]许录平.数值图像处理[M].北京:科学出版社,2007.
© 2004-2018 中国地质图书馆版权所有 京ICP备05064691号 京公网安备11010802017129号 地址：北京市海淀区学院路29号 邮编：100083 电话：办公室：(+86 10)66554848；文献借阅、咨询服务、科技查新：66554700 |