Share Email Print
cover

Proceedings Paper

Segmentation of brain PET-CT images based on adaptive use of complementary information
Author(s): Yong Xia; Lingfeng Wen; Stefan Eberl; Michael Fulham; Dagan Feng
Format Member Price Non-Member Price
PDF $14.40 $18.00
cover GOOD NEWS! Your organization subscribes to the SPIE Digital Library. You may be able to download this paper for free. Check Access

Paper Abstract

Dual modality PET-CT imaging provides aligned anatomical (CT) and functional (PET) images in a single scanning session, which can potentially be used to improve image segmentation of PET-CT data. The ability to distinguish structures for segmentation is a function of structure and modality and varies across voxels. Thus optimal contribution of a particular modality to segmentation is spatially variant. Existing segmentation algorithms, however, seldom account for this characteristic of PET-CT data and the results using these algorithms are not optimal. In this study, we propose a relative discrimination index (RDI) to characterize the relative abilities of PET and CT to correctly classify each voxel into the correct structure for segmentation. The definition of RDI is based on the information entropy of the probability distribution of the voxel's class label. If the class label derived from CT data for a particular voxel has more certainty than that derived from PET data, the corresponding RDI will have a higher value. We applied the RDI matrix to balance adaptively the contributions of PET and CT data to segmentation of brain PET-CT images on a voxel-by-voxel basis, with the aim to give the modality with higher discriminatory power a larger weight. The resultant segmentation approach is distinguished from traditional approaches by its innovative and adaptive use of the dual-modality information. We compared our approach to the non-RDI version and two commonly used PET-only based segmentation algorithms for simulation and clinical data. Our results show that the RDI matrix markedly improved PET-CT image segmentation.

Paper Details

Date Published: 27 March 2009
PDF: 8 pages
Proc. SPIE 7259, Medical Imaging 2009: Image Processing, 72593A (27 March 2009); doi: 10.1117/12.811078
Show Author Affiliations
Yong Xia, The Univ. of Sydney (Australia)
Hong Kong Polytechnic Univ. (Hong Kong, China)
Lingfeng Wen, The Univ. of Sydney (Australia)
Hong Kong Polytechnic Univ. (Hong Kong, China)
Royal Prince Alfred Hospital (Australia)
Stefan Eberl, The Univ. of Sydney (Australia)
Royal Prince Alfred Hospital (Australia)
Michael Fulham, The Univ. of Sydney (Australia)
Royal Prince Alfred Hospital (Australia)
Dagan Feng, The Univ. of Sydney (Australia)
Hong Kong Polytechnic Univ. (Hong Kong, China)


Published in SPIE Proceedings Vol. 7259:
Medical Imaging 2009: Image Processing
Josien P. W. Pluim; Benoit M. Dawant, Editor(s)

© SPIE. Terms of Use
Back to Top