|
|
|
| Fast Image Segmentation Model Combined with Fuzzy C-means Method and Curve Evolution |
| MA Yingran PENG Yanjun |
| (College of Information Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, China) |
|
|
|
|
Abstract To solve the problems about noise sensitivity and unclosed segmentation object boundaries in Fuzzy C-means Method (FCM), this paper proposes a fast image segmentation model combined with FCM and curve evolution, based on the pseudo level set formulations and object boundary curves, which are defined on the membership matrixes of FCM. To get the smooth and closed segmentation object boundaries, the Gaussian filter is performed on the pseudo level sets to approximate the function of the curve length regularization term. To eliminate the influence of Gaussian filter on the results of FCM, the gray values of the noisy points?are corrected, according to a new introduced edge-stop function and the mapping relationship between the gray value and membership degree. The FCM and the smoothing object boundary stage are performed alternately, which improves the robustness of this model. The experimental results show that the proposed model can overcome the influence of noise and get better segmentation results.
|
|
Received: 22 July 2016
Published: 20 March 2017
|
|
|
| Fund: The National Natural Science Foundation of China (61502279), The National Key Research and Development Projects (2016YFC0801406), Shandong Provincial Natural Science Foundation (ZR2015FM013), Shandong Province Key Research and Development Projects (2016GSF120012), Taishan Scholar Project |
|
Corresponding Authors:
PENG Yanjun
E-mail: pengyanjuncn@163.com
|
|
|
|
| [1] |
CASELLES V, KIMMEL G, and SAPIRO G. Geodesic active contours[C]. Proceedings of IEEE International Computer Vision, Cambridge, Massachusetts, 1995: 694-699. doi: 10.1109/ICCV.1995.466871.
|
| [2] |
YANG M S and TSAI H S. A Gaussian kernel-based fuzzy c-means algorithm with a spatial bias correction[J]. Pattern Recognition Letters, 2008, 29(12): 1713-1725. doi: 10. 1016/j.patrec.2008.04.016.
|
| [3] |
韩明, 刘教民, 孟军英, 等. 结合局部能量与改进的符号距离正则项的图像目标分割算法[J]. 电子与信息学报, 2015, 37(9): 2047-2054. doi: 10.11999/JEIT141473.
|
|
HAN Ming, LIU Jiaomin, MENG Junying, et al. Local energy information combined with improved signed distance regularization term for image target segmentation algorithm[J]. Journal of Electronics & Information Technology, 2015, 37(9): 2047-2054. doi: 10.11999/ JEIT141473.
|
| [4] |
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. Multi-objective evolutionary clustering with complementary spatial information for image segmentation[J]. Journal of Electronics & Information Technology, 2015, 37(3): 672-678. doi: 10.11999/JEIT140371.
|
| [5] |
OSHER S and SETHIAN J A. Fronts propagating with curvature-dependent speed: Algori-thms based on Hamilton-Jacobi formulations[J]. Journal of Computational Physics, 1988, 79(1): 12-49. doi: 10.1016/0021-9991(88) 90002-2.
|
| [6] |
ADALSTEINSSON D and SETHIAN A. A fast level set method for propagating inter-faces[J]. Journal of Computational Physics, 1995, 118(2): 269-277. doi: 10.1006/ jcph.1995.1098.
|
| [7] |
WHITAKER R T. A level-set approach to 3D reconstruction from range data[J]. International Journal of Computer Vision, 1998, 29(3): 203-231. doi: 10.1023/A:1008036829907.
|
| [8] |
SHI Y G and KARL W C. A real-time algorithm for approximation of level-set-basedcurve evolution[J]. IEEE Transactions on Image Processing, 2008, 17(5): 645-656. doi: 10.1109/TIP.2008.920737.
|
| [9] |
AHMED M N, YAMANY S M, MOHAMED N, et al. A modified fuzzy C-means algo-rithm for bias field estimation and segmentation of MIR data[J]. IEEE Transactions on Medical Imaging, 2002, 21(3): 193-199. doi: 10.1109/42. 996338.
|
| [10] |
李阳, 庞永杰, 盛明伟. 结合空间信息的模糊聚类侧扫声纳图像分割[J]. 中国图象图形学报, 2015, 20(7): 865-870. doi: 10.11834/jig.20150702.
|
|
LI Yang, PANG Yongjie, and SHENG Mingwei. Side-scan sonar image segmentation via fuzzy clustering with spatial constrains[J]. Journal of Image and Graphics, 2015, 20(7): 865-870. doi: 10.11834/jig.20150702.
|
| [11] |
唐利明, 田学全, 黄大荣, 等. 结合FCMS与变分水平集的图像分割模型[J]. 自动化学报, 2014, 40(6): 1233-1248. doi: 10.3724/SP.J.1004.2014.01233.
|
|
TANG Liming, TIAN Xuequan, HUANG Darong, et al. Image segmentation model com-bined with FCMS and variational level set[J]. Acta Automatica Sinica, 2014, 40(6): 1233-1248. doi: 10.3724/SP.J.1004.2014.01233.
|
| [12] |
SAMSON C, BLANC-FERAUD L, AUBERT G, et al. A level set model for image clas-sification[J]. Journal of Computer Vision, 2000, 40(3): 187-197. doi: 10.1023/A:1008183109594.
|
| [13] |
LIN S, GAO M T, WANG S M, et al. An image segmentation method by combining fuzzy c-means clustering and graph cuts optimization for multiphase level set algorithms[C]. 2015 2nd International Conference on Information Science and Control Engineering, Shanghai, China, 2015: 611-615.
|
| [14] |
WANG L, MA Y R, ZHAN K, et al. Automatic left ventricle segmentation in cardiac MRI via level set and fuzzy C-means[C]. 2015 2nd International Conference on Recent Advances in Engineering & Computational Sciences, Chandigarh, India, 2015: 1-6.
|
| [15] |
唐利明, 王洪珂, 陈照辉, 等. 基于变分水平集的图像模糊聚类分割[J]. 软件学报, 2014, 25(7): 1570-1582. doi: 10.13328/ j.cnki.jos.004449.
|
|
TANG Liming, WANG Hongke, CHEN Zhaohui, et al. Image fuzzy clustering segmenta-tion based on variational level set[J]. Journal of Software, 2014, 25(7): 1570-1582. doi: 10.13328/j.cnki.jos.004449.
|
| [16] |
KRINIDIS S and CHATZIS V. Fuzzy energy-based active contours[J]. IEEE Transactions on Image Processing, 2009, 18(12): 2747-2755. doi: 10.1109/TIP.2009.2030468.
|
| [17] |
ZHANG Kaihua, ZHANG Lei, LAM K M, et al. A level set approach to image segmen-tation with intensity inhomogeneity[J]. IEEE Transactions on Cybernetics, 2016, 46(2): 546-557. doi: 10.1109/TCYB.2015.2409119.
|
|
|
|
