[1]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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Medical image segmentation based on FCM with peak detection

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 9 Number of periods: 2014 5 Page number: 584-589 Column: 学术论文—机器学习 Public date: 2014-10-25
Title:
Medical image segmentation based on FCM with peak detection
Author(s):
TANG Wenjing1;  XU Zhaoxin2;  ZHANG Xiaofeng1
1. College of Information and Electrical Engineering, Ludong University, Yantai 264025, China;
2. College of Automation, Harbin Engineering University, Harbin 150001, China
Keywords:
FCM; FCMs; EnFCM; image segmentation; medical image processing; peak detection; clustering centers; histogram
CLC:
TP391.4
DOI:
10.3969/j.issn.1673-4785.201408007
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.
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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