Medical image retrieval by high level semantic features and low level content features of image.
- Author:
Tianwen XIE
1
;
Weijun TANG
;
Qiufeng ZHAO
;
Jiaao ZHAO
Author Information
1. Telemedicine Center, Fudan University, Shanghai 200032, China.
- Publication Type:Journal Article
- MeSH:
Algorithms;
Artificial Intelligence;
Diagnostic Imaging;
Humans;
Image Processing, Computer-Assisted;
methods;
Information Storage and Retrieval;
Radiology Information Systems;
Systems Integration;
User-Computer Interface
- From:
Journal of Biomedical Engineering
2009;26(6):1237-1240
- CountryChina
- Language:Chinese
-
Abstract:
Content-based image retrieval aims at searching the similar images using low level features,and medical image retrieval needs it for the retrieval of similar images. Medical images contain not only a lot of content data, but also a lot of semantic information. This paper presents an approach by combining digital imaging and communications in medicine (DICOM) features and low level features to perform retrieval on medical image databases. At the first step, the semantic information is extracted from DICOM header for the pre-filtering of the images, and then dual-tree complex wavelet transfrom(DT-CWT) features of pre-filtered images and example images are extracted to retrieve similar images. Experimental results show that by combining the high level semantics (DICOM features) and low level content features (texture) the retrieval time is reduced and the performance of medical image retrieval is increased.
