Learning Based Combining Different Features for Medical Image Retrieval

被引:0
作者
Zhi Lijia [1 ]
Zhang Shaomin [1 ]
Zhao Dazhe [1 ,2 ]
Yu Hongfei [1 ]
Zhao Hong [1 ,2 ]
Lin Shukuan [1 ]
Zhao Dazhe [1 ,2 ]
Zhao Hong [1 ,2 ]
机构
[1] Northeastern Univ, Coll Informat Sci & Engn, Shenyang 110004, Liaoning, Peoples R China
[2] Natl Digital Med Imaging Equipment, Engn Technol Res Ctr, Shenyang 110004, Peoples R China
来源
PROCEEDINGS OF THE FIFTH INTERNATIONAL CONFERENCE ON IMAGE AND GRAPHICS (ICIG 2009) | 2009年
关键词
medical image retrieval; global features; local features; feature combination; learning;
D O I
10.1109/ICIG.2009.33
中图分类号
TP301 [理论、方法];
学科分类号
081202 ;
摘要
In this paper, authors propose a new learning based method for medical image retrieval which is based on fusing different features by linearly combining different similarities. Considering the abundant classes of medical images, this paper avoid to train a classifier for each class by using large amount training data. Instead, by using optimization method to combine different features' similarity, new method can get good performance while has no much training computation. Experimental results show that the algorithm has potential practical values for clinical routine application.
引用
收藏
页码:969 / 972
页数:4
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