抑制式非局部空间直觉模糊C-均值图像分割算法
|
摘要
针对传统的模糊C-均值(FCM)算法没有考虑图像像素的空间邻域信息,对噪声敏感,算法收敛较慢等问题,该文提出一种抑制式非局部空间直觉模糊C-均值图像分割算法。首先,通过计算像素的非局部空间信息提高抗噪能力,克服传统的FCM算法只考虑图像单个像素的灰度特征信息的缺陷,提高分割精度。其次,根据直觉模糊集理论,通过"投票模型"自适应生成犹豫度作为抑制因子修正隶属度,提高算法的运行效率。实验结果表明,该算法对噪声鲁棒性较强并且有较好的分割性能。
In order to deal with these issues of the traditional Fuzzy C-Means(FCM) algorithm, such as without consideration of the spatial neighborhood information of pixels, noise sensitivity and low convergence speed, a suppressed non-local spatial intuitionistic fuzzy c-means image segmentation algorithm is proposed.Firstly, in order to improve the accuracy of segmentation image, the non-local spatial information of pixel is used to improve anti-noise ability, and to overcome the shortcomings of the traditional FCM algorithm, which only considers the gray characteristic information of single pixel. Secondly, by using the ‘voting model' based on the intuitionistic fuzzy set theory, the hesitation degrees are adaptively generated as inhibitory factors to modify the membership degrees, and then the operating efficiency is increased. Experimental results show that the new algorithm is robust to noise and has better segmentation performance.
In order to deal with these issues of the traditional Fuzzy C-Means(FCM) algorithm, such as without consideration of the spatial neighborhood information of pixels, noise sensitivity and low convergence speed, a suppressed non-local spatial intuitionistic fuzzy c-means image segmentation algorithm is proposed.Firstly, in order to improve the accuracy of segmentation image, the non-local spatial information of pixel is used to improve anti-noise ability, and to overcome the shortcomings of the traditional FCM algorithm, which only considers the gray characteristic information of single pixel. Secondly, by using the ‘voting model' based on the intuitionistic fuzzy set theory, the hesitation degrees are adaptively generated as inhibitory factors to modify the membership degrees, and then the operating efficiency is increased. Experimental results show that the new algorithm is robust to noise and has better segmentation performance.
引文
[1]吴方,何尾莲.基于改进粗糙集概率模型的鲁棒医学图像分割算法[J].计算机应用研究,2017,34(8):2546-2550.doi:10.3969/j.issn.1001-3695.2017.08.069.WU Fang and HE Weilian.Improved probability model of rough set based robust medical image segmentation algorithm[J].Application Research of Computers,2017,34(8):2546-2550.doi:10.3969/j.issn.1001-3695.2017.08.069.
[2]缪立军,车自远.基于自适应下采样的移动机器人视觉定位技术[J].应用光学,2017,38(3):429-433.doi:10.5768/JAO201738.0302008.MIAO Lijun and CHE Ziyuan.Visual locating of mobile robot based on adaptive down sampling[J].Journal of Applied Optics,2017,38(3):429-433.doi:10.5768/JAO201738.0302008.
[3]张飞龙,王顺芳,赵剑华,等.基于图像分割及模糊隶属度的PCA人脸识别[J].计算机应用与软件,2014,31(5):188-190.doi:10.3969/j.issn.1000-386x.2014.05.048.ZHANG Feilong,WANG Shunfang,ZHAO Jianhua,et al.Face recognition with PCA based on image segmentation and fuzzy membership[J].Computer Application and Software,2014,31(5):188-190.doi:10.3969/j.issn.1000-386x.2014.05.048.
[4]纪星波,张海峰.改进的指纹自适应阈值分割算法[J].杭州电子科技大学学报(自然科学版),2015,35(2):65-69.doi:10.13954/j.cnki.hdu.2015.02.016.JIN Xingbo and ZHANG Haifeng.The improved algorithm of fingerprint segmentation based on adaptive threshold[J].Journal of Hanzhou Dianzi University(Natural Sciences),2015,35(2):65-69.doi:10.13954/j.cnki.hdu.2015.02.016.
[5]张博,倪开灶,王林军,等.基于背景矫正和图像分割定量分析光学元件表面疵病的新算法[J].光学学报,2016,36(9):120-129.doi:10.3788/AOS201636.0911004.ZHANG Bo,NI Kaizao,WANG Linjun,et al.New algorithm of detecting optical surface imperfection based on background correction and image segmentation[J].Acta Optica Sinica,2016,36(9):120-129.doi:10.3788/AOS201636.0911004.
[6]申铉京,刘翔,陈海鹏.基于多阈值Ostu准则的阈值分割快速计算[J].电子与信息学报,2017,39(1):144-149.doi:10.11999/JEIT160248.SHEN Xuanjing,LIU Xiang,and CHEN Haipeng.Fast computation of threshold based on multi-threshold Ostu criterion[J].Journal of Electronics&Information Technology,2017,39(1):144-149.doi:10.11999/JEIT160248.
[7]肖明尧,李雄飞,张小利,等.基于多尺度的区域生长的图像分割算法[J].吉林大学学报(工学版),2017,5(47):1591-1597.doi:10.13229/j.cnki.jdxbgxb201705035.XIAO Mingyao,LI Xiongfei,ZHANG Xiaoli,et al.Medical image segmentation algorithm based on multi-scale region growing[J].Journal of Jilin University(Engineering and Technology Edition),2017,5(47):1591-1597.doi:10.13229/j.cnki.jdxbgxb201705035.
[8]刘永学,李春满,毛亮.基于边缘的多光谱遥感图像分割方法[J].遥感学报,2006,10(3):350-356.LIU Yongxue,LI Chunman,and MAO Liang.An algorithm of multi-spectral remote image segmentation based on edge information[J].Journal of Remote Sensing,2006,10(3):350-356.
[9]赵凤,刘汉强,范九伦.基于互补空间信息的多目标进化聚类图像分割[J].电子与信息学报,2015,37(3):672-678.doi:10.11999/JEIT140371.ZHAO Feng,LIU Hanqiang,and FAN Jiulun.Multiobjective evolutionary clustering with complementary spatial information for image segmentation[J].Journal of Electronics&Information Technology,2015,37(3):672-678.doi:10.11999/JEIT140371.
[10]FAN Jiulun,ZHEN Wenzhi,and XIE Weixin.Suppressed fuzzy C-means clustering algorithm[J].Pattern Recognition Letter,2003,24(9/10):1607-1612.
[11]兰蓉,马姣婷.基于直觉模糊C-均值聚类算法的图像分割[J].西安邮电大学学报,2016,21(3):1-4.doi:10.13682/j.issn.2095-6533.2016.04.010.LAN Rong and MA Jiaoting.Image segmentation based on intuitionstic fuzzy c-means clustering algorithm[J].Journal of Xi’an University of Posts and Telecommunications,2016,21 (3):1-4.doi:10.13682/j.issn.2095-6533.2016.04.010.
[12]AHMED M N,YAMANY S M,MOHAMED N,et al.Amodified fuzzy c-means algorithm for bias filed estimation and segmentation of MRI data[J].IEEE Transactions on Medical Imaging,2002,21(3):193-199.doi:10.1109/42.996338.
[13]CHEN S C and ZHANG D Q.Robust image segmentation using FCM with spatial constraints based on new kernelinduced distance measure[J].IEEE Transactions on Systems,Man and Cybernetics.Part B:Cybernetics,2004,34(4):1907-1916.doi:10.1109/TSMCB.2004.831165.
[14]ZHAO Feng,JIAO Licheng,and LIU Hanqiang.Fuzzy cmeans clustering with non local spatial information for noise image segmentation[J].Frontiers of Computer Science in China,2011,5(1):45-56.doi:10.1007/s11704-010-0393-8.
[15]范九伦.抑制式模糊C-均值聚类研究综述[J].西安邮电大学学报,2014,19(3):1-5.doi:10.13682/j.issn.2095-6533.2014.03.001.FAN Jiulun.A brief overview on suppressed fuzzy C-means clustering[J].Journal of Xi’an University of Posts and Telecommunications,2014,19(3):1-5.doi:10.13682/j.issn.2095-6533.2014.03.001.
[16]BUADES A,COLL B,and MOREL J M.A non-local algorithm for image denoising[C].Proceeding of IEEEInternational Conference on Computer Vision and Pattern Recognition,San Diego,USA,2005:60-65.
[17]ATANASSOV K T.Intuitionistic fuzzy sets[J].Fuzzy Sets and Systems,1986,20(1):87-96.doi:10.1016/S0165-0114(86)80034-3.
[18]赵凤.基于模糊聚类的图像分割[M].西安:西安电子科技大学出版社,2015:43.ZHAO Feng.Fuzzy Clustering for Image Segmentation[M].Xi’an:Publisher of Xidian University,2015:43.
[19]LAN Rong,FAN Jiulun,LIU Ying,et al.Image thresholding by maximizing the similarity degree based on intuitionistic fuzzy sets[C].Quantitative Logic and Soft Computing,Hangzhou,China,2016:631-640.
[20]ZHAO Feng,JIAO Licheng and LIU Hanqiang.Amultiobjective spatial fuzzy clustering algorithm for image segmentation[J].Applied Soft Computing,2015,30:48-57.doi:10.1016/j.asoc.2015.01.039.
[21]XIE Xuanli and BENI G.A validity measure for fuzzy clustering[J].IEEE Transactions on Pattern Analysis&Machine Intelligence,1991,13(13):841-847.
[22]DICE L R.Measures of the amount of ecologic association between species[J].Ecology,1945,26(3):297-302.doi:10.2307/1932409.
[2]缪立军,车自远.基于自适应下采样的移动机器人视觉定位技术[J].应用光学,2017,38(3):429-433.doi:10.5768/JAO201738.0302008.MIAO Lijun and CHE Ziyuan.Visual locating of mobile robot based on adaptive down sampling[J].Journal of Applied Optics,2017,38(3):429-433.doi:10.5768/JAO201738.0302008.
[3]张飞龙,王顺芳,赵剑华,等.基于图像分割及模糊隶属度的PCA人脸识别[J].计算机应用与软件,2014,31(5):188-190.doi:10.3969/j.issn.1000-386x.2014.05.048.ZHANG Feilong,WANG Shunfang,ZHAO Jianhua,et al.Face recognition with PCA based on image segmentation and fuzzy membership[J].Computer Application and Software,2014,31(5):188-190.doi:10.3969/j.issn.1000-386x.2014.05.048.
[4]纪星波,张海峰.改进的指纹自适应阈值分割算法[J].杭州电子科技大学学报(自然科学版),2015,35(2):65-69.doi:10.13954/j.cnki.hdu.2015.02.016.JIN Xingbo and ZHANG Haifeng.The improved algorithm of fingerprint segmentation based on adaptive threshold[J].Journal of Hanzhou Dianzi University(Natural Sciences),2015,35(2):65-69.doi:10.13954/j.cnki.hdu.2015.02.016.
[5]张博,倪开灶,王林军,等.基于背景矫正和图像分割定量分析光学元件表面疵病的新算法[J].光学学报,2016,36(9):120-129.doi:10.3788/AOS201636.0911004.ZHANG Bo,NI Kaizao,WANG Linjun,et al.New algorithm of detecting optical surface imperfection based on background correction and image segmentation[J].Acta Optica Sinica,2016,36(9):120-129.doi:10.3788/AOS201636.0911004.
[6]申铉京,刘翔,陈海鹏.基于多阈值Ostu准则的阈值分割快速计算[J].电子与信息学报,2017,39(1):144-149.doi:10.11999/JEIT160248.SHEN Xuanjing,LIU Xiang,and CHEN Haipeng.Fast computation of threshold based on multi-threshold Ostu criterion[J].Journal of Electronics&Information Technology,2017,39(1):144-149.doi:10.11999/JEIT160248.
[7]肖明尧,李雄飞,张小利,等.基于多尺度的区域生长的图像分割算法[J].吉林大学学报(工学版),2017,5(47):1591-1597.doi:10.13229/j.cnki.jdxbgxb201705035.XIAO Mingyao,LI Xiongfei,ZHANG Xiaoli,et al.Medical image segmentation algorithm based on multi-scale region growing[J].Journal of Jilin University(Engineering and Technology Edition),2017,5(47):1591-1597.doi:10.13229/j.cnki.jdxbgxb201705035.
[8]刘永学,李春满,毛亮.基于边缘的多光谱遥感图像分割方法[J].遥感学报,2006,10(3):350-356.LIU Yongxue,LI Chunman,and MAO Liang.An algorithm of multi-spectral remote image segmentation based on edge information[J].Journal of Remote Sensing,2006,10(3):350-356.
[9]赵凤,刘汉强,范九伦.基于互补空间信息的多目标进化聚类图像分割[J].电子与信息学报,2015,37(3):672-678.doi:10.11999/JEIT140371.ZHAO Feng,LIU Hanqiang,and FAN Jiulun.Multiobjective evolutionary clustering with complementary spatial information for image segmentation[J].Journal of Electronics&Information Technology,2015,37(3):672-678.doi:10.11999/JEIT140371.
[10]FAN Jiulun,ZHEN Wenzhi,and XIE Weixin.Suppressed fuzzy C-means clustering algorithm[J].Pattern Recognition Letter,2003,24(9/10):1607-1612.
[11]兰蓉,马姣婷.基于直觉模糊C-均值聚类算法的图像分割[J].西安邮电大学学报,2016,21(3):1-4.doi:10.13682/j.issn.2095-6533.2016.04.010.LAN Rong and MA Jiaoting.Image segmentation based on intuitionstic fuzzy c-means clustering algorithm[J].Journal of Xi’an University of Posts and Telecommunications,2016,21 (3):1-4.doi:10.13682/j.issn.2095-6533.2016.04.010.
[12]AHMED M N,YAMANY S M,MOHAMED N,et al.Amodified fuzzy c-means algorithm for bias filed estimation and segmentation of MRI data[J].IEEE Transactions on Medical Imaging,2002,21(3):193-199.doi:10.1109/42.996338.
[13]CHEN S C and ZHANG D Q.Robust image segmentation using FCM with spatial constraints based on new kernelinduced distance measure[J].IEEE Transactions on Systems,Man and Cybernetics.Part B:Cybernetics,2004,34(4):1907-1916.doi:10.1109/TSMCB.2004.831165.
[14]ZHAO Feng,JIAO Licheng,and LIU Hanqiang.Fuzzy cmeans clustering with non local spatial information for noise image segmentation[J].Frontiers of Computer Science in China,2011,5(1):45-56.doi:10.1007/s11704-010-0393-8.
[15]范九伦.抑制式模糊C-均值聚类研究综述[J].西安邮电大学学报,2014,19(3):1-5.doi:10.13682/j.issn.2095-6533.2014.03.001.FAN Jiulun.A brief overview on suppressed fuzzy C-means clustering[J].Journal of Xi’an University of Posts and Telecommunications,2014,19(3):1-5.doi:10.13682/j.issn.2095-6533.2014.03.001.
[16]BUADES A,COLL B,and MOREL J M.A non-local algorithm for image denoising[C].Proceeding of IEEEInternational Conference on Computer Vision and Pattern Recognition,San Diego,USA,2005:60-65.
[17]ATANASSOV K T.Intuitionistic fuzzy sets[J].Fuzzy Sets and Systems,1986,20(1):87-96.doi:10.1016/S0165-0114(86)80034-3.
[18]赵凤.基于模糊聚类的图像分割[M].西安:西安电子科技大学出版社,2015:43.ZHAO Feng.Fuzzy Clustering for Image Segmentation[M].Xi’an:Publisher of Xidian University,2015:43.
[19]LAN Rong,FAN Jiulun,LIU Ying,et al.Image thresholding by maximizing the similarity degree based on intuitionistic fuzzy sets[C].Quantitative Logic and Soft Computing,Hangzhou,China,2016:631-640.
[20]ZHAO Feng,JIAO Licheng and LIU Hanqiang.Amultiobjective spatial fuzzy clustering algorithm for image segmentation[J].Applied Soft Computing,2015,30:48-57.doi:10.1016/j.asoc.2015.01.039.
[21]XIE Xuanli and BENI G.A validity measure for fuzzy clustering[J].IEEE Transactions on Pattern Analysis&Machine Intelligence,1991,13(13):841-847.
[22]DICE L R.Measures of the amount of ecologic association between species[J].Ecology,1945,26(3):297-302.doi:10.2307/1932409.