A robust volumetric feature extraction approach for 3D neuroimaging retrieval

Sidong Liu; Weidong Cai; Lingfeng Wen; Stefan Eberl; Michael J Fulham; Dagan Feng

doi:10.1109/IEMBS.2010.5627900

A robust volumetric feature extraction approach for 3D neuroimaging retrieval

Annu Int Conf IEEE Eng Med Biol Soc. 2010:2010:5657-60. doi: 10.1109/IEMBS.2010.5627900.

Authors

Sidong Liu¹, Weidong Cai, Lingfeng Wen, Stefan Eberl, Michael J Fulham, Dagan Feng

Affiliation

¹ Biomedical and Multimedia Information Technology (BMIT) Research Group, School of Information Technologies, University of Sydney, Australia.

PMID: 21097311
DOI: 10.1109/IEMBS.2010.5627900

Abstract

The increased volume of 3D neuroimaging data has created a need for efficient data management and retrieval. We suggest that image retrieval via robust volumetric features could benefit managing these large image datasets. In this paper, we introduce a new feature extraction method, based on disorder-oriented masks, that uses the volumetric spatial distribution patterns in 3D physiological parametric neurological images. Our preliminary results indicate that the proposed volumetric feature extraction approach could support reliable 3D neuroimaging data retrieval and management.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Aged
Algorithms*
Databases, Factual
Dementia / diagnosis*
Diagnostic Imaging / methods*
Female
Humans
Imaging, Three-Dimensional / methods*
Information Storage and Retrieval / methods*
Male
Middle Aged
Positron-Emission Tomography