New method for extracting region of interest of dynamic PET images based on curve clustering

doi:10.3724/SP.J.1087.2012.00535

Journal of Computer Applications ›› 2012, Vol. 32 ›› Issue (02): 535-550.DOI: 10.3724/SP.J.1087.2012.00535

• Graphics and image technology • Previous Articles Next Articles

New method for extracting region of interest of dynamic PET images based on curve clustering

TIAN Ping-ping¹,LIU Li¹,²,CHEN Yu-ting³

1. Department of Information, Wuxi Fourth People's Hospital, Wuxi Jiangsu 214122, China
2. Department of Information, Wuxi Fourth People's Hospital, Wuxi Jiangsu 214122, China
3. School of IoT Engineering, Jiangnan University, Wuxi Jiangsu 214122, China

Received:2011-07-05 Revised:2011-09-19 Online:2012-02-23 Published:2012-02-01
Contact: LIU Li

基于曲线聚类的动态PET影像感兴趣区域提取新方法

田平平¹,刘丽¹,²,陈玉婷¹

1. 江南大学物联网工程学院，江苏无锡 214122
2. 无锡市第四人民医院信息科，江苏无锡 214062

通讯作者: 刘丽
作者简介:
田平平(1987-),女，江苏连云港人，硕士研究生，主要研究方向：医学影像处理；
刘丽(1975-)，女，天津人，副教授，博士，主要研究方向：人工免疫、医学影像处理；
陈玉婷(1986-)，女，江苏盐城人，硕士研究生，主要研究方向：人工免疫、医学影像定量分析。
基金资助:
中央高校基本科研业务费专项资金资助项目(JUSRP10928);无锡市科技支撑社会发展项目(CSE01014);卫生部核医学重点实验室，江苏省分子核医学重点实验室开放课题(KF201104)

Abstract

Abstract: Concerning the problem that many current clustering methods based on kinetic characteristics ignore the continuous temporal information of Time Activity Curve (TAC), a method for Region Of Interest (ROI) extraction based on curve clustering was proposed in the paper. The proposed method contains three steps. Firstly, K-Means algorithm was used to remove the background to obtain a coarse mask of the heart. Secondly, curve clustering was used to extract myocardium from the heart obtained in the first step. Finally, blood cavity was delineated based on spatial relationship between pixels. The method was applied to extract the ROI from fourteen mouse PET images. The experimental results indicate that the proposed method is more accurate in delineating blood cavity of the fourteen mice than K-Means and Hybrid Clustering Method (HCM), and it is more precise and stable.

Key words: curve clustering, kinetic characteristic, Region Of Interest (ROI) extraction, dynamic PET image

摘要： 针对目前动态正电子发射断层扫描(PET)影像的感兴趣区域(ROI)提取的聚类方法忽略了时间放射性曲线(TAC)的时间序列特征，提出一种基于曲线聚类的ROI提取方法。首先用K-均值(K-Means)聚类去除背景得到心脏的位置,然后对心脏进行曲线聚类提取出心肌，最后根据像素点的空间位置关系提取血池。将该方法应用于14只小鼠的PET影像ROI勾画，实验结果表明，与K-Means和混合型的聚类方法HCM相比，该方法能够更准确地提取出14只小鼠的血池，且具有更高的精确度和稳定性。

关键词: 曲线聚类, 动力学特征, 感兴趣区域提取, 动态PET影像

CLC Number:

TP391.41

TIAN Ping-ping LIU Li CHEN Yu-ting. New method for extracting region of interest of dynamic PET images based on curve clustering[J]. Journal of Computer Applications, 2012, 32(02): 535-550.

田平平刘丽陈玉婷. 基于曲线聚类的动态PET影像感兴趣区域提取新方法[J]. 计算机应用, 2012, 32(02): 535-550.

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New method for extracting region of interest of dynamic PET images based on curve clustering

基于曲线聚类的动态PET影像感兴趣区域提取新方法

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