title: Unsupervised tumour segmentation in PET using local and global intensity-fitting active surface and alpha matting.
abstract: This paper proposes an unsupervised tumour segmentation approach for PET data. The method computes the volumes of interest (VOIs) with sub-voxel precision by considering the limited image resolution and partial volume effects. First, an improved anisotropic diffusion filter is used to remove image noise. A hierarchical local and global intensity active surface modelling scheme is then applied to segment VOIs, followed by an alpha matting step to further refine the segmentation boundary. The proposed method is validated on real PET images of head-and-neck cancer patients with ground truth provided by human experts, as well as custom-designed phantom PET images with objective ground truth. Experimental results show that our method outperforms previous automatic approaches in terms of segmentation accuracy.
authors.affiliation: Department of Computer Science, Aberystwyth University, Aberystwyth, UK; Faculty of Information and Control Engineering, Shenyang Jianzhu University, Shenyang, China. Electronic address: zengziming1983@gmail.com.
authors.forename: Ziming
authors.initials: Z
authors.lastname: Zeng
authors.forename: Jue
authors.initials: J
authors.lastname: Wang
authors.forename: Bernie
authors.initials: B
authors.lastname: Tiddeman
authors.forename: Reyer
authors.initials: R
authors.lastname: Zwiggelaar
mesh_terms: D006258:Head and Neck Neoplasms / Q000000981:diagnostic imaging*; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008954:Models, Biological; D019047:Phantoms, Imaging; D049268:Positron-Emission Tomography / Q000379:methods*; D012372:ROC Curve
keywords: Active surface modelling; Alpha matting; Mutual information; Positron emission tomography; Tumour segmentation