[1]唐文静,许兆新,张小峰.峰值检测FCM算法的医学图像分割[J].智能系统学报,2014,9(5):584-589.[doi:10.3969/j.issn.1673-4785.201408007]
TANG Wenjing,XU Zhaoxin,ZHANG Xiaofeng.Medical image segmentation based on FCM with peak detection[J].CAAI Transactions on Intelligent Systems,2014,9(5):584-589.[doi:10.3969/j.issn.1673-4785.201408007]
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TANG Wenjing,XU Zhaoxin,ZHANG Xiaofeng.Medical image segmentation based on FCM with peak detection[J].CAAI Transactions on Intelligent Systems,2014,9(5):584-589.[doi:10.3969/j.issn.1673-4785.201408007]
峰值检测FCM算法的医学图像分割
《智能系统学报》[ISSN 1673-4785/CN 23-1538/TP] 卷:
9
期数:
2014年第5期
页码:
584-589
栏目:
学术论文—机器学习
出版日期:
2014-10-25
- Title:
- Medical image segmentation based on FCM with peak detection
- Keywords:
- FCM; FCMs; EnFCM; image segmentation; medical image processing; peak detection; clustering centers; histogram
- 分类号:
- TP391.4
- 摘要:
- 为了更好地平衡传统FCM及其相关改进算法的分割效果与分割效率问题,提出了一种基于峰值检测的快速FCM图像分割算法。首先基于峰值检测策略对聚类中心进行初始化;然后在初始化聚类中心的基础上对医学图像进行分割。其本质是运用峰值检测技术指导聚类中心的初始化,以使初始化的聚类中心尽可能靠近最终的聚类中心,从而以提高算法的工作效率。在医学图像上进行的实验表明,算法可以有效地提高图像分割的效率,并能得到很好的分割效果。
- Abstract:
- In order to balance the segmentation results and efficiency of traditional FCM and related improved algorithms, a fast FCM segmentation scheme based on peak detection is proposed in this paper. First the cluster centroids are initialized based on peak detection strategy, and then the medical image segmentation is performed based on the initial cluster centroids. The nature of the proposed scheme is to guide the initialization of cluster centroids with peak detection, which can make the initial centroids close to the final centroids and further improve the efficiency of the algorithm. Experiments on the medical images showed that the proposed scheme can improve the segmentation efficiency greatly and obtain good segmentation results.
- 参考文献/References:
-
[1] 徐少平, 刘小平, 李春泉, 等. 基于区域特征分析的快速FCM图像分割改进算法[J].模式识别与人工智能, 2012, 25(6):987-995.XU Shaoping, LIU Xiaoping, LI Chunquan, et al. An improved fast FCM image segmentation algorithm based on region feature analysis[J]. Pattem Recognition and Aitificial Intelligence, 2012, 25(6):987-995.
[2] HE Lianghua, WEN Ying, WAN Meng, et al. Multi-channel features based automated segmentation of diffusion tensor imaging using an improved FCM with spatial constraints[J]. Neurocomputing, 2014, 137:107-114.
[3] 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.TANG Liming, TIAN Xuequan, HUANG Darong, et al. Image segmentation model combined with FCMS and variational level set[J]. Acta Automatica Sinica, 2014, 40(6):1233-1248.
[4] CAO H B, DENG H W, WANG Y P. Segmentation of M-FISH images for improved classification of chromosomes with an adaptive fuzzy C-means clustering algorithm[J]. IEEE Transactions on Fuzzy Systems, 2012, 20(1):1-8.
[5] 张小峰. 基于模糊聚类算法的医学图像分割技术研究 [D]. 济南:山东大学, 2014:7-20.ZHANG Xiaofeng. Research of medical image segmentation based on fuzzy clustering algorithm [D].Jinan:Shandong University, 2014:7-20.
[6] KANNAN S R, RAMATHILAGAM S, SATHYA A, et al. Effective fuzzy C-means based kernel function in segmenting medical images[J]. Computers in Biology and Medicine, 2010, 40(6):572-579.
[7] AHMED M N, YAMANY S M, MOHAMED N, et al. A modified fuzzy c-means algorithm for bias field estimation and segmentation of MRI data[J]. IEEE Trans Med Imaging, 2002, 21(3):193-199.
[8] CHUANG K S, TZENG H L, CHEN S, et al. Fuzzy C-means clustering with spatial information for image segmentation[J]. Computerized Medical Imaging and Graphics, 2006, 30(1):9-15.
[9] SZILAGYI L, BENYO Z, SZILAGYI S M, et al. MR brain image segmentation using an enhanced fuzzy c-means algorithm [C].//Proceedings of 25th Annual International Conference of IEEE EMBS. Cancun, Mexico, 2003:17-21.
[10] 张保威, 钱慎一, 宋宝卫. 改进FCM在医学图像分割中的应用[J]. 计算机工程, 2012, 38(14):193-195.ZHANG Baowei, QIAN Shenyi, SONG Baowei. Application of improved FCM in medical image segmentation[J]. Computer Engineering, 2012, 38(14):193-195.
[11] 吴林, 郭大勇, 施克仁等. 改进的FCM在人脑MR图像分割中的应用[J]. 清华大学学报:自然科学版, 2004, 44(2):157-159.WU Lin, GUO Dayong, SHI Keren, et al. Modified fuzzy c-means algorithm for image segmentation in brain magnetic resonance images[J]. Journal Tsinghua University:Sci & Tech, 2004, 44(2):157-159.
[12] MILIND M, MUSHIRIF, AJOY K. A-IFS histon based multithresholding algorithm for color image segmentation[J]. IEEE Signal Processing Letters, 2009, 16(3):168-171.
[13] LI Yang, YU Fusheng. A new validity function for fuzzy clustering[C]//Proceedings of the 2009 International Conference on Computational Intelligence and Natural Computing. Park City, USA, 2009:462-465.
[14] ZHANG Daoqiang, CHEN Songcan. A novel kernelized fuzzy C-means algorithm with application in medical image segmentation[J]. Artificial Intelligence in Medicine, 2004, 32(1):37-50.
[15] BEZDEK J C. Cluster validity with fuzzy sets[J]. Cybernetics, 1974, 3(3):58-73.
[16] XIE X L, BENIi G. A validity measure for fuzzy clustering[J]. IEEE Trans Pattern Anal Mach Intell, 1991, 13(8):841-847.
[17] LIU J Q, YANG Y H. Multi-resolution color image segmentation[J]. IEEE Trans Pattern Anal Mach Intell, 1994, 16(7):689-700.
[18] WITOLD P. Knowledge-based clustering[M]. Wiley-Interscience, 2005:46-66.
[19] GRAVES D, PEDRYCZ W. Kernel-based fuzzy clustering and fuzzy clustering:a comparative experimental study[J]. Fuzzy Sets and Systems, 2010, 161(4):522-543.
[20] 贾旭, 崔建江, 薛定宇, 等. 基于感兴趣区域函数优化的静脉图像分割算法[J]. 模式识别与人工智能, 2012, 25(3):475-480.JIA Xu, CUI Jianjiang, XUE Dingyu, et al. Vein image segmentation algorithm based on function optimization in regions of interest[J]. Pattem Recognition and Aitificial Intelligence, 2012, 25(3):475-480.
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备注/Memo
收稿日期:2014-08-04。
基金项目:国家自然科学基金资助项目(61170161);山东省自然科学基金资助项目(ZR2012FQ029);鲁东大学基金资助项目(LY2010014).
作者简介:许兆新, 1966年生, 女, 研究员, 博士, 主要研究方向为信息处理与控制、智能航海。完成预研、专项及与其他科研单位合作项目等10余项。获国防科学技术奖一等奖1项, 军队科技进步二等奖1项, 其他省级科技进步奖等多项, 发表学术论文20余篇, 出版专著1部;张小峰, 1978年生, 男, 讲师, 博士, 主要研究方向为模式识别、数字图像处理。主持和参与多项省部级课题, 曾获烟台市科研论文一等奖, 发表学术论文20余篇, 多篇被SCI、EI收录。
通讯作者:唐文静, 1980年生, 女, 讲师, 博士, 主要研究方向为图像处理、模式识别。主持山东省自然科学基金项目1项, 参与国家自然科学基金项目1项、山东省自然科学基金项目1项, 发表学术论文十余篇, 出版专著1部。E-mail:twj_tang@126.com.
更新日期/Last Update:
1900-01-01