Please use this identifier to cite or link to this item:
Title: Variational bayes inference based segmentation of heterogeneous lymphoma volumes in dual-modality PET-CT images
Authors: Wang, J
Xia, Y
Wang, J
Feng, D
Keywords: PET-CT
Variational bayes inference (VBI)
Tumour segmentation
Issue Date: 2011
Source: Proceedings of the International Conference on Digital Image Computing Techniques and Applications (DICTA'2011), Noosa, Queensland, Australia, 6-8 Dec. 2011, p. 274-278 How to cite?
Abstract: Accurate segmentation of heterogeneous carcinoma lesions in medical images is vital to the treatment planning, assessment of therapy response and other oncological applications. With current state-of-the-art imaging modalities, the CT images enhance the interpretation of cancer functional abnormalities. We applied the variational Bayes inference (VBI) model on both anatomical and functional information for delineating lesion boundary. The model is improved by clinical meaningful initialisation. Clinical data consisting of eight lesions with inhomogeneous carcinoma distribution were used to evaluate the model accuracy. Our algorithm is capable of isolating lesions from background with higher accuracy comparing to the wildly used threshold (40% of SUVmax). The VBI segmentation error is less than 6.11% ± 4.92% which is much better than the results performed by fixed threshold method. The experimental results show that our novel statistic method can produce more accurate segmentation of heterogeneous lymphoma volume in PET-CT images.
ISBN: 978-1-4577-2006-2
DOI: 10.1109/DICTA.2011.52
Appears in Collections:Conference Paper

View full-text via PolyU eLinks SFX Query
Show full item record

Page view(s)

Last Week
Last month
Citations as of Mar 19, 2018

Google ScholarTM



Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.