面向多纹理特征的脑瘤图像分割方法
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摘要
为了研究医学脑瘤图像纹理特征的选取和平滑图像分割轮廓线的问题,依据纳什均衡理论,给出了纳什均衡的多纹理特征计算方法和表示公式;并在纳什均衡多纹理计算的基础上,给出了纳什均衡计算的相似区域的判断与相似区域的合并方法,提出了用于图像分割轮廓线平滑的面向多纹理特征的改进的C-V模型。脑瘤图像分割实验结果表明:本文方法与较典型的纹理图像分割方法相比,脑瘤图像分割准确率平均提高5%,验证了本文方法的有效性。
Texture image segmentation is a key technology in image processing. To study the selection of textural features and smoothing of contour lines in medical brain tumor images,Nash equilibrium multi-texture feature calculation method and expression formula are presented on the basis of Nash equilibrium theory. Then,based on Nash equilibrium multi-texture calculation,the method for judging the similarity and merging similarity regions of the Nash equilibrium calculation is given. An improved cross-validation model for multi-texture features in image segmentation contour smoothing is proposed. The image segmentation experiment shows that compared with the typical texture image segmentation method,the proposed method is more effective because its accuracy is 5% higher on average.
Texture image segmentation is a key technology in image processing. To study the selection of textural features and smoothing of contour lines in medical brain tumor images,Nash equilibrium multi-texture feature calculation method and expression formula are presented on the basis of Nash equilibrium theory. Then,based on Nash equilibrium multi-texture calculation,the method for judging the similarity and merging similarity regions of the Nash equilibrium calculation is given. An improved cross-validation model for multi-texture features in image segmentation contour smoothing is proposed. The image segmentation experiment shows that compared with the typical texture image segmentation method,the proposed method is more effective because its accuracy is 5% higher on average.
引文
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[20]王翠杰,杨永红,林明.基于改进C-V模型的高分辨SAR图像分割[J].电子设计工程,2017,25(19):161-164.WANG Cuijie,YANG Yonghong,LIN Ming. High-resolution SAR image segmentation based on improved C-V model[J]. Electronic design engineering, 2017, 25(19):161-164.
[21]DING W,WANG J,DONG J,et al. A quantum-inspired cooperative co-evolution based minimum attribute reduction algorithm and its application in medical MRI[J]. Journal of information and computational science,2012,9(5):1125-1133.
[22]张石,董建威,佘黎煌.医学图像分割算法的评价方法[J].中国图象图形学报,2009,14(9):1872-1880.ZHANG Shi,DONG Jianwei,SHE Lihuang. The methodology of evaluating segmentation algorithms on medical image[J]. Journal of image and graphics,2009,14(9):1872-1880.
[23]LI Changyang,WANG Xiuying,EBERL S,et al. Supervised variational model with statistical inference and its application in medical image segmentation[J]. IEEE transactions on biomedical engineering, 2015, 62(1):196-207.
[24]BEEVI Z,SATHIK M. A robust segmentation approach for noisy medical images using fuzzy clustering with spatial probability[J]. International Arab journal of information technology,2009,9(1):74-83.
[25]程旸,蒋亦樟,钱鹏江,等.知识迁移的极大熵聚类算法及其在纹理图像分割中的应用[J].智能系统学报,2017,12(2):179-187.CHENG Yang,JIANG Yizhang,QIAN Pengjiang,A maximum entropy clustering algorithm based on knowledge transfer and its application to texture image segmentation[J]. CAAI transactions on intelligent systems,2017,12(2):179-187.
[2]李中海.一种综合多种方法的纹理图像分割[D].大连:大连理工大学,2008:12-13.LI Zhonghai. Texture image segmentation based on a synthetic approach[D]. Dalian:Dalian University of Technology,2008:12-13.
[3]胡永强,宋良图,张洁,等.基于稀疏表示的多特征融合害虫图像识别[J].模式识别与人工智能,2014,27(11):985-992.HU Yongqiang,SONG Liangtu,ZHANG Jie,et al. Pest image recognition of multi-feature fusion based on sparse representation[J]. Pattern recognition and artificial intelligence,2014,27(11):985-992.
[4]张翔,邓赵红,王士同,等.极大熵Relief特征加权[J].计算机研究与发展,2011,48(6):1038-1048.ZHANG Xiang,DENG Zhaohong,WANG Shitong,et al.Maximum entropy relief feature weighting[J]. Journal of computer research and development,2011,48(6):1038-1048.
[5]ZHANG Kun,KONG Weiren,LIU Peipei,et al. Partition region-based suppressed fuzzy C-means algorithm[J].Journal of systems engineering and electronics,2017,28(5):996-1008.
[6]全卫澎,李卫华,李小春,等.基于统计与频谱模型特征融合的纹理图像分割[J].科学技术与工程,2014,14(19):106-111.QUAN Weipeng,LI Weihua,LI Xiaochun,et al. A segmentation algorithm for texture based on statistical and spectrum model texture information weighting[J]. Science technology and engineering,2014,14(19):106-111.
[7]赵泉华,高郡,李玉.基于区域划分的多特征纹理图像分割[J].仪器仪表学报,2015,36(11):2519-2530.ZHAO Quanhua,GAO Jun,LI Yu. Multi-feature texture image segmentation based on tessellation technique[J].Chinese journal of scientific instrument,2015,36(11):2519-2530.
[8]袁宝峰,吴乐华,曾伟.基于纹理与灰度协同进化的图像分割算法[J].计算机应用,2009,29(1):54-56.YUAN Baofeng,WU Lehua,ZENG Wei. Image segmentation algorithm based on coevolution with texture and gray scale[J]. Journal of computer applications,2009,29(1):54-56.
[9]黄宁宁,贾振红,杨杰,等.基于GLCM和EM算法的纹理图像分割[J].通信技术,2011,44(1):48-49,52.HUANG Ningning,JIA Zhenhong,YANG Jie,et al. Texture segmentation based on gray-level co-occurrence matrix features and EM algorithm[J]. Communications technology,2011,44(1):48-49,52.
[10]SINGH M,SINGH S,GUPTA S. An information fusion based method for liver classification using texture analysis of ultrasound images[J]. Information fusion,2014,19:91-96.
[11]王亭,王向阳.基于纹理测度与自适应阈值的FCM图像分割算法[J].小型微型计算机系统,2010,31(6):1209-1212.WANG Ting,WANG Xiangyang. New FCM image segmentation based on texture measure and adaptive threshold[J]. Journal of Chinese computer systems, 2010,31(6):1209-1212.
[12]张佳明,张荣国,赵建,等.基于纹理特征的主动轮廓模型图像分割[J].太原科技大学学报,2017,38(3):190-195.ZHANG Jiaming,ZHANG Rongguo,ZHAO Jian,et al.Image segmentation of active contour model based on texture feasure[J]. Journal of Taiyuan university of science and technology,2017,38(3):190-195.
[13]林瑶,田捷.医学图像分割方法综述[J].模式识别与人工智能,2002,15(2):192-113.LIN Yao,TIAN Jie. A survey on medical image segmentation methods[J]. Pattern recognition and artificial intelligence,2002,15(2):192-113.
[14]隗立涛,修乃华.基于启发搜索算法的纳什均衡计算[J].北京交通大学学报,2007,31(3):58-62.WEI Litao,XIU Naihua. Computing Nash equilibria based on heuristic search methods[J]. Journal of Beijing Jiaotong University,2007,31(3):58-62.
[15]RESKA D,BOLDAK C,KRETOWSKI M. Towards multi-stage texture-based active contour image segmentation[J]. Signal,image and video processing,2017,11(5):809-816.
[16]NABIZADEH N,KUBAT M. Automatic tumor segmentation in single-spectral MRI using a texture-based and contour-based algorithm[J]. Expert systems with applications,2017,77:1-10.
[17]卢芸.基于阈值分割的织物疵点检测方法研究与实现[D].石家庄:河北科技大学,2008:22-24.LU Yun. Research and implementation of fabric defects detection method based on threshold segmentation[D].Shijiazhuang:Hebei University of Science&Technology,2008:22-24.
[18]徐玲凌,肖进胜,易本顺,等.改进的C-V水平集模型图像分割算法[J].计算机工程,2009,35(10):209-210,213.XU Lingling,XIAO Jinsheng,YI Benshun,et al. Improved C-V level set model image segmentation algorithm[J]. Computer engineering,2009,35(10):209-210,213.
[19]付金明,羿旭明,檀伟伟,等.基于改进C-V水平集模型的SAR图像分割[J].数学杂志,2016,36(3):649-654.FU Jinming,YI Xuming,TAN Weiwei,et al. SAR image segmentation based on improved level SET C-V model[J]. Journal of mathematics,2016,36(3):649-654.
[20]王翠杰,杨永红,林明.基于改进C-V模型的高分辨SAR图像分割[J].电子设计工程,2017,25(19):161-164.WANG Cuijie,YANG Yonghong,LIN Ming. High-resolution SAR image segmentation based on improved C-V model[J]. Electronic design engineering, 2017, 25(19):161-164.
[21]DING W,WANG J,DONG J,et al. A quantum-inspired cooperative co-evolution based minimum attribute reduction algorithm and its application in medical MRI[J]. Journal of information and computational science,2012,9(5):1125-1133.
[22]张石,董建威,佘黎煌.医学图像分割算法的评价方法[J].中国图象图形学报,2009,14(9):1872-1880.ZHANG Shi,DONG Jianwei,SHE Lihuang. The methodology of evaluating segmentation algorithms on medical image[J]. Journal of image and graphics,2009,14(9):1872-1880.
[23]LI Changyang,WANG Xiuying,EBERL S,et al. Supervised variational model with statistical inference and its application in medical image segmentation[J]. IEEE transactions on biomedical engineering, 2015, 62(1):196-207.
[24]BEEVI Z,SATHIK M. A robust segmentation approach for noisy medical images using fuzzy clustering with spatial probability[J]. International Arab journal of information technology,2009,9(1):74-83.
[25]程旸,蒋亦樟,钱鹏江,等.知识迁移的极大熵聚类算法及其在纹理图像分割中的应用[J].智能系统学报,2017,12(2):179-187.CHENG Yang,JIANG Yizhang,QIAN Pengjiang,A maximum entropy clustering algorithm based on knowledge transfer and its application to texture image segmentation[J]. CAAI transactions on intelligent systems,2017,12(2):179-187.