@inproceedings{7dde0817529f4b0bb3d6fba2e769727d,
title = "Unsupervised tumour segmentation in PET based on local and global intensity fitting active surface and alpha matting",
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.",
author = "Ziming Zeng and Tony Shepherd and Reyer Zwiggelaar",
year = "2012",
month = aug,
day = "28",
doi = "10.1109/EMBC.2012.6346432",
language = "English",
isbn = "978-1-4244-4119-8",
series = "Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS",
publisher = "IEEE Press",
pages = "2339--2342",
booktitle = "2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2012",
address = "United States of America",
}