|本期目录/Table of Contents|

[1]柯杉,王博亮*,黄晓阳.一种改进的SIFT算法及其在医学图像配准中的应用[J].厦门大学学报(自然科学版),2010,49(03):354.
 KE Shan,WANG Bo liang*,HUANG Xiao yang.An Improved SIFT Algorithm and Its Application in Medical Image Registration[J].Journal of Xiamen University(Natural Science),2010,49(03):354.
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一种改进的SIFT算法及其在医学图像配准中的应用(PDF)
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《厦门大学学报(自然科学版)》[ISSN:0438-0479/CN:35-1070/N]

卷:
49卷
期数:
2010年03期
页码:
354
栏目:
研究论文
出版日期:
2010-05-20

文章信息/Info

Title:
An Improved SIFT Algorithm and Its Application in Medical Image Registration
作者:
柯杉王博亮*黄晓阳
厦门大学信息科学与技术学院,福建 厦门 361005
Author(s):
KE ShanWANG Boliang*HUANG Xiaoyang
School of Information Science and Technology,Xiamen University,Xiamen 361005,China
关键词:
医学图像配准特征点SIFT 算法
Keywords:
medical image registrationkeypointSIFT algorithm
分类号:
TP 391.41
文献标志码:
-
摘要:
图像特征点的提取是医学图像配准的基础,其精确性直接影响匹配的结果.目前在实际应用中常使用手工提取特征点的方法,精确性差且工作量大.SIFT算子具有良好的尺度、旋转、光照等不变特性,被广泛应用于图像配准中.由于SIFT匹配算法对特征点匹配的条件较为严格,特征点的数量常常无法满足医学图像配准的实际需要,并且存在一定的误匹配.为增加特征点的数量,提高匹配准确率,采用SIFT算法自动提取特征点,并使用特征点之间的Euclid距离作为相似性判定度量,根据医学图像的特点保留低对比度点,以实现医学图像的配准.实验结果表明该方法是有效的.
Abstract:
Feature extraction is the basis of medical image registration.The accuracy of feature points directly affects matching result.It often use manpower to do feature extraction at present,but the accuracy is poor and the workload is heavy.The features extracted with SIFT are invariant to image scale and rotation,and provide robust matching.SIFT algorithm has been widely used in image registration.As the SIFT algorithm is very strict about matching condition,the number of feature points often can not meet the needs of medical image registration,and there are some false matches to a certain extent. In order to increase the number of feature points and improve the matching accuracy, the Euclid distance has been adopted to determine the similarity between feature points and low contrast points have been retained according to the characteristics of medical images. The experiment results demonstrate the effectiveness of this method.

参考文献/References:


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备注/Memo

备注/Memo:
收稿日期:20091104基金项目:国家自然科学基金(60701022,30770561);卫生部科学研究基金福建省卫生教育联合攻关计划资助项目(WKJ20082041)*通讯作者:blwang@xmu.edu.cn
更新日期/Last Update: 2010-05-20