结合局部信息改进的C-V超声图像分割模型

针对无边缘主动轮廓模型 (Active contours without edges, C-V)难以分割灰度分布不均匀的甲状腺超声图像, 本文提出结合局部信息改进的 C-V超声图像分割模型。该方法根据局部信息具有不受灰度分布影响的拟合特性, 利用图像局部拟合信息构造一种新的速度函数, 使速度函数依据图像局部灰度变化控制曲线的演化速率；然后将该速度函数引入到 C-V模型中, 具有全局分割能力。实验结果表明, 本文方法可以实现对灰度分布不均匀的甲状腺肿瘤超声图像的准确分割, 且分割效率也有所提高。

Abstract

As active contours without edges (C-V) model is difficult to segment thyroid ultrasound image with intensity inhomogeneity. Therefore, the improved C-V ultrasound image segmentation model of combining local information is proposed. First, the local information is not affected by the gray distribution. So, through this characteristic, we constructed a new speed function by using local image fitting information. According to the change of local gray level, the speed function can flexibly control curve evolution rate. Then, the speed function was incorporated into the C-V model, and had the ability of global segmentation. The experiment results demonstrate that the proposed model can achieve accurate segmentation for thyroid tumor ultrasound image with intensity inhomogeneity. And the segmentation efficiency is also improved.

参考文献

[1] Chan T, Vese L. Active contours without edges [J]. IEEE Transactions on Image Processing(S1057-7149), 2001, 10(2): 266-277.

[2] CHEN Li, ZHOU Yue. GACV: geodesic-aided C-V method [J]. Pattern Recognition(S0031-3203), 2006, 39(7): 1391-1395.

[3] ZHANG Kaihua, ZHANG Lei, SONG Huihui, et al. Active contours with selective local or global segmentation: A new formulation and level set method [J]. Image and Vision Computing(S0262-8856), 2010, 28(4): 668-676.

[4] Alipour S, Shanbehzadeh J. Fast automatic medical image segmentation based on spatial kernel fuzzy c-means on level set method [J]. Machine Vision and Applications(S0932-8092), 2014, 25: 1469-1488.

[5] Piovano J, Rousson M, Papadopoulo T. Efficient segmentation of piecewise smooth images [J]. Scale Space and Variational Methods in Computer Vision(S0302-9743), 2007, 44(85): 709-720.

[6] LI Chunming, KAO Chuiyen, Gore J C, et al. Implicit active contours driven by local binary fitting energy [C]// IEEE Comference on Computer Vision and Pattern Recognition, Washington, USA, 2007: 1-7.

[7] LI Chunming, KAO Chuiyen, Gore J C, et al. Minimization of region-scalable fitting energy for image segmentation [J]. IEEE Transactions on Image Processing(S1057-7149), 2008, 17(10): 1940-1949.

[8] WANG Li, LI Chunming, SUN Quansen, et al. Active contours driven by local and global intensity fitting energy with application to brain MR image segmentation [J]. Computerized Medical Imaging and Graphics(S0895-6111), 2009, 33(7): 520-531.

[9] 刘瑞娟, 何传江, 原野. 融合局部和全局图像信息的活动轮廓模型 [J].计算机辅助设计与图形学学报, 2012, 24(3): 364-371. LIU Ruijuan, HE Chuanjiang, YUAN Ye. Active contours driven by local and global image fitting energy [J]. Journal of Computer-aided Design & Computer Graphics, 2012, 24(3): 364-371.

[10] 张建伟, 方林, 陈允杰, 等. 基于活动轮廓模型的左心室 MR图像分割 [J].电子学报, 2011, 39(11): 2670-2673. ZHANG Jianwei, FANG Lin, CHEN Yunjie, et al. Left ventricle MRI segmentation based on active contour model [J]. Acta Electronica Sinica, 2011, 39(11): 2670-2673.

[11] 王相海, 金弋博. 高光谱海岸带区域分割的活动轮廓模型 [J].中国图象图形学报, 2013, 18(8): 1031-1037. WANG Xianghai, JIN Yibo. The active contour model for segmentation of coastal hyperspectral remote sensing image [J]. Journal of Image and Graphics, 2013, 18(8): 1031-1037.

[12] CHEN Kan, LI Bin, TIAN Lianfang, et al. Fuzzy speed function-based active contour model for segmentation of pulmonary nodules [J]. Bio-Medical Materials and Engineering(S0959-2989), 2014, 24(1): 539-547.

[13] LI Chunming, XU Chenyang, GUI Changfeng, et al. Distance regularized level set evolution and its application to image segmentation [J]. IEEE Transactions on Image Processing(S1057-7149), 2010, 19(12): 3243-3254.

[14] Vovk U, Pernus F, Likar B. A review of methods for correction of intensity inhomogeneity in MRI [J]. IEEE Transactions on Medical Imaging(S0278-0062), 2007, 26(3): 405-421.

[15] Collins D L, Holms C J, Peters T M, et al. Automatic 3-D model-based neuroanatomical segmentation [J]. Human Brain Mapping(S1065-9471), 1995, 3(3): 190-208.

郑伟, 张晶, 李凯玄, 郝冬梅. 结合局部信息改进的C-V超声图像分割模型[J]. 光电工程, 2015, 42(8): 66. ZHENG Wei, ZHANG Jing, LI Kaixuan, HAO Dongmei. The Improved C-V Ultrasound Image Segmentation Model of Combining Local Information[J]. Opto-Electronic Engineering, 2015, 42(8): 66.

结合局部信息改进的C-V超声图像分割模型

关于本站 Cookie 的使用提示

全站搜索

结合局部信息改进的C-V超声图像分割模型

相关论文

相关资讯

关于本站 Cookie 的使用提示

全站搜索