Using human perceptual categories for content-based retrieval from a medical image database
Computer Vision and Image Understanding
Molecular imaging and biomedical process modeling
APBC '04 Proceedings of the second conference on Asia-Pacific bioinformatics - Volume 29
A similarity measure based on causal neighbours and mutual information
Design and application of hybrid intelligent systems
Automatic detection of PET lesions
VIP '02 Selected papers from the 2002 Pan-Sydney workshop on Visualisation - Volume 22
Semantics and CBIR: a medical imaging perspective
CIVR '08 Proceedings of the 2008 international conference on Content-based image and video retrieval
Prototype System for Semantic Retrieval of Neurological PET Images
Medical Imaging and Informatics
A Fast Approach to Segmentation of PET Brain Images for Extraction of Features
Medical Imaging and Informatics
Content-based image database system for epilepsy
Computer Methods and Programs in Biomedicine
A pattern similarity scheme for medical image retrieval
IEEE Transactions on Information Technology in Biomedicine - Special section on biomedical informatics
Content based image retrieval from chest radiography databases
Asilomar'09 Proceedings of the 43rd Asilomar conference on Signals, systems and computers
| Hi-index | 0.00 |
The recent information explosion has led to a massively increased demand for multimedia data storage in integrated database systems. Content-based retrieval is an important alternative and complement to traditional keyword-based searching for multimedia data and can greatly enhance information management. However, current content-based image retrieval techniques have some deficiencies when applied in the biomedical functional imaging domain. In this paper, we presented a prototype design for a content-based functional image retrieval database system for dynamic positron emission tomography (PET). The system supports efficient content-based retrieval based on physiological kinetic features and reduces image storage requirements. This design makes it possible to maintain a large number of patient data sets online and to rapidly retrieve dynamic functional image sequences for the interpretation and generation of physiological parametric images, and offers potential advantages in medical image data management and telemedicine, as well as providing possible opportunities in the statistical and comparative analysis of functional image data.