基于空间信息的FCM医学图像高效分割

›› 2011, Vol. 24 ›› Issue (7): 72-.

基于空间信息的FCM医学图像高效分割

王黎明

(西安电子科技大学电子工程学院,陕西西安 710071)

出版日期:2011-07-15 发布日期:2011-08-16
作者简介:王黎明(1985—),女,硕士研究生。研究方向:数字图像及医学影像处理方向。

FCM Algorithm with Spatial Information for Effective Medical Image Segmentation

WANG Li-Ming

(School of Electronic Engineering,Xidian University,Xi'an 710071,China)

Online:2011-07-15 Published:2011-08-16

摘要/Abstract

摘要：

标准FCM算法仅考虑图像的灰度信息,没有考虑图像的空间信息,所以对噪声比较敏感。考虑到医学图像数据提取中必定包含噪声,因此设计的算法必须对噪声具有鲁棒性。文中算法在KFCM_S2的基础上加入模糊空间信息,利用邻域像素对当前像素作用的先验概率,重新确定当前像素的模糊隶属度,同时进一步地调整距离矩阵。为实现快速聚类,算法的开始进行直方图初始化。实验结果表明,相对于标准的FCM和KFCM_S2算法,文中算法既能快速有效地分割图像,又能提高对噪声的鲁棒性。

关键词: 模糊C均值(FCM), 空间信息, 医学图像分割

Abstract:

Standard FCM algorithm takes into account the gray information,not the spatial information of the image,so the algorithm is sensitive to noise.In view of the fact that medical image data must contain much noise in the process of acquisition,the designed algorithm must be robust to noise.The improved algorithm is formulated by incorporating fuzzy spatial information into KFCM_S2,a prior probability is given to indicate the spatial influence of the neighboring pixels on the centre pixel,the new fuzzy membership of the current pixel is then recounted with the obtained probability,and then adjust the distance matrix.To realize fast clustering,the histogram initialization is used at the beginning of the algorithm.Experiment results show that the improved algorithm is more efficient and more robust to noise than the standard FCM and KFCM_S2.

Key words: Fuzzy c-means(FCM);spatial information;medical image segmentation

中图分类号:

TN919.8

王黎明. 基于空间信息的FCM医学图像高效分割[J]. , 2011, 24(7): 72-.

WANG Li-Ming. FCM Algorithm with Spatial Information for Effective Medical Image Segmentation[J]. , 2011, 24(7): 72-.

参考文献

［1］LIN Y,TIAN J.A survey on medical image segmentation methods［J］.Pattern Recognition and Artificial Intelligence,2002,15(2):192-204.

［2］PAL N R,PAL S K.A review on image segmentation tech-niques［J］.Pattern Recognition,1993,26(9):1277-1294.

［3］PHARN D L,XU C Y,PRINCE J L.A survey of current methods in medical image segmentation［R］.Technical Report JHU/ECE99-01,Johns Hopkins University,1998.

［4］FRANCESCO M,ANDREA S.A fuzzy clustering based segmentation system as support diagnosis in medical imaging［J］.Artif Intel in Med,1999,16(2):129-147.

［5］聂生东,陈瑛,顾顺德,等.磁共振颅脑图像快速模糊聚类分割算法的研究［J］.中国生物医学工程学报,2001,20(2):104-109.

［6］CHEN S C,ZHANG D Q.Robust image segmentation using fcm with spatial constraints based on new kernel-induced distance measure［J］.IEEE Trans.Systems Man Cybernet,B,2004,34(4):1907-1916.

［7］AHMEND M N,YAMANY S M,MOHAMED N.A modified fuzzy c-means algorithm for bias field estimation and segmentation of MRI data［J］.IEEE Trans.on Medical Imaging,2002,(21):193-199.

［8］CHUANG K S,TZENG H L,CHEN S W.Fuzzy C-means clustering with spatial information for image segmentation ［J］.Computerized Medical Imaging and Graphic,2006(30):9-15.

［9］Zhang D Q,Chen S C,Pan Z S.Kernel-based fuzzy clustering incorporating spatial constraints for image segmentation［C］.Proc.International Conference on Machine Learning and Cybennetics,2003(4):2189-2192.

［10］BEZDEK J C,PAL S K.Fuzzy models for pattern recognition［M］.Piscataway,NJ,USA:IEEE Press,1999.

［11］SEZGIN M,SANKUR B.Survey over image thresholding techniques and quantitative performance evaluation［J］.Journal of Electronic Imaging,2004,13(1):146-165.

［12］COLLINS D L,ZIJDENBOS A P,KOLLOKIAN V.Design and construction of a realistic digital brain phantom［J］.IEEE Trans Med Imag,1998,17(3):463-468.

[1]	房巾莉,吕毅斌,王樱子,唐胜男,武德安. 基于水平集的医学图像分割算法[J]. 电子科技, 2021, 34(2): 12-20.
[2]	何菁，陈胜. 一种全新的两步自动化医学图像分割方案[J]. , 2016, 29(7): 85-.
[3]	戴橙,陈胜. 改进的分水岭算法用于X光医学图像分割[J]. , 2015, 28(6): 9-.