|本期目录/Table of Contents|

[1]高岩,王博亮*.改进的区域生长算法及其在肾实质自动分割中的应用[J].厦门大学学报(自然科学版),2012,51(4):701.
 GAO Yan,WANG Bo liang*.An Improved Region Growing Algorithm and Its Applications in Kidney Segmentation[J].Journal of Xiamen University(Natural Science),2012,51(4):701.
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改进的区域生长算法及其在肾实质自动分割中的应用(PDF)
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《厦门大学学报(自然科学版)》[ISSN:0438-0479/CN:35-1070/N]

卷:
51卷
期数:
2012年第4期
页码:
701
栏目:
研究论文
出版日期:
2012-07-15

文章信息/Info

Title:
An Improved Region Growing Algorithm and Its Applications in Kidney Segmentation
作者:
高岩王博亮*
厦门大学信息科学与技术学院,福建 厦门 361005
Author(s):
GAO YanWANG Boliang*
School of Information Science and Technology,Xiamen University,Xiamen 361005,China
关键词:
图像分割直方图特征区域生长邻域相似性指标
Keywords:
image segmentationhistogram featuresregion growingneighbor similarity factor
分类号:
R 318;TP 391
文献标志码:
-
摘要:
提出了一种肾脏CT图像自动分割方法,将像素点的局部统计特征和像素点的空间位置信息结合起来,以此定义了像素之间的邻域相似性指标,并根据领域相似性指标自动选取种子点、种子的生长准则及终止准则,该方法克服了传统区域生长算法需手动确定种子点和生长顺序固定等缺点,最后通过MICCAI(medical image computing and computer assited intervention)的5个评价指标对分割结果做出客观评价,结果表明,该算法具有较好的分割效果.
Abstract:
An improved region growing algorithm for automatic segmentation kidney from abdominal CT image is proposed.Compared to original region growing method,this method automatically selected initial seedpixels and is robust to the order of region growing.Firstly,computing the neighbor similarity factor (NSF) based on local histogram of each pixel and spatial information of local pixels.Then,building the criteria of initial seedpixels,region growth and region growing termination based on NSF.Finally,MICCAI metrics are adopted to measure the segmentation accuracy.Experimental results demonstrated the performance of method.

参考文献/References:

[1]Senthilkumaran N,Rajesh R.Edge detection techniques for image segmentationa survey[J].International Journal of Recent Trends in Engineering,2009,1(2):749760. [2]Senthilkumaran N,Rajesh R.Edge detection techniques for image segmentation and a survey of soft computing approaches[J].International Journal of Recent Trends in Engineering,2009,1(2):250254. [3]Baillard C,Hellier P,Barillot C.Segmentation of brain 3D MR images using level sets and dense registration[J].Medical Image Analysis,2001,5(3):185194 . [4]Hao X H,Bruce C,Pislaru C,et al.A novel region growing method for segmenting ultrasound images[J].IEEE Ultrasonics Symposium Proceedings,2000,2:17171720. [5]Sergyan S.Color histogram features based image classification in contentbased image retrieval systems[C]//2008 6th International Symposium on Applied Machine Intelligence and Informatics.New York:IEEE,2008:206209. [6]Ding Jundi,Ma Runing,Chen Songcan,et al.A scalebased connected coherence tree algorithm for image segmentation [J].IEEE Transactions on Image Processing,2008,17(2):204216. [7]Deng X,Du G.Editorial:3D segmentation in the clinic:a grand challenge IIliver tumor segmentation[C]// Presented at Workshop proceedings of the 11th International Conference on Medical Image Computing and Computer Assisted InterventionMICCAI 2008,Workshop on 3D Segmentation in the Clinic :a Grand Challenge II.New York,USA:[s.n.],2008:14.

备注/Memo

备注/Memo:
收稿日期:20120105基金项目:国家自然科学基金项目(30770561,61071151,61102137)*通信作者:blwang@jingxian.xmu.edu.cn
更新日期/Last Update: 2012-07-15