Unsupervised tumour segmentation in PET based on local and global intensity fitting active surface and alpha matting

Ziming Zeng, Tony Shepherd, Reyer Zwiggelaar

Research output: Chapter in Book/Report/Conference proceedingConference Proceeding (Non-Journal item)

2 Citations (Scopus)

Abstract

This paper proposes an unsupervised tumour segmentation scheme for PET data. The method computes the volume of interests (VOIs) with subpixel precision by considering the limited resolution and partial volume effect. Firstly, it uses local and global intensity active surface modelling to segment VOIs, then an alpha matting method is used to eliminate false negative classification and refine the segmentation results. We have validated our method on real PET images of head-and-neck cancer patients as well as images of a custom designed PET phantom. Experiments show that our method can generate more accurate segmentation results compared with alternative approaches.
Original languageEnglish
Title of host publication2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2012
Subtitle of host publicationEMBC 2012
PublisherIEEE Press
Pages2339-2342
Number of pages4
ISBN (Electronic)978-1-4577-1787-1
ISBN (Print)978-1-4244-4119-8
DOIs
Publication statusPublished - 28 Aug 2012

Publication series

NameProceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
ISSN (Print)1557-170X

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