A fast approach to segmentation of PET brain images for extraction of features

Gao, Xiaohong W. and Clark, John (2008) A fast approach to segmentation of PET brain images for extraction of features. In: Medical imaging and informatics. Gao, Xiaohong W. and Loomes, Martin J. and Comley, Richard A. and Muller, Henning and Luo, Shuqian, eds. Lecture Notes in Computer Science (4987). Springer, Berlin, pp. 197-206. ISBN 3794521935

Full text is not in this repository.

Official URL: http://dx.doi.org/10.1007/978-3-540-79490-5_25

Abstract

Position Emission Tomography (PET) is increasingly applied in the diagnosis and surgery in patients thanks to its ability of showing nearly all types of lesions including tumour and head injury. However, due to its natures of low resolution and different appearances as a result of different tracers, segmentation of lesions presents great challenges. In this study, a simple and robust algorithm is proposed via additive colour mixture approach. Comparison with the other two methods including Bayesian classified and geodesic active contour is also performed, demonstrating the proposed colouring approach has many advantages in terms of speed, robustness, and user intervention. This research has many medical applications including pharmaceutical trials, decision making for drug treatment or surgery and patients follow-up and shows potential to the development of content-based image databases when coming to characterise PET images using lesion features.

Item Type:Book Section
Additional Information:

2nd International Conference, MIMI 2007, Beijing, China, August 14-16, 2007.

Research Areas:Middlesex University Schools and Centres > School of Science and Technology > Computer Science
Middlesex University Schools and Centres > School of Science and Technology > Computer Science > Artificial Intelligence group
ID Code:1765
Useful Links:
Deposited On:01 Apr 2009 10:07
Last Modified:24 Oct 2014 15:18

Repository staff only: item control page

Full text downloads (NB count will be zero if no full text documents are attached to the record)

Downloads per month over the past year