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

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.

[1]  Xiaodong Wu,et al.  Comparative study with new accuracy metrics for target volume contouring in PET image guided radiation therapy. , 2012, IEEE transactions on medical imaging.

[2]  Reyer Zwiggelaar,et al.  Hierarchical modelling for unsupervised tumour segmentation in PET , 2012, Proceedings of 2012 IEEE-EMBS International Conference on Biomedical and Health Informatics.

[3]  Chunming Li,et al.  Brain MR Image Segmentation Using Local and Global Intensity Fitting Active Contours/Surfaces , 2008, MICCAI.

[4]  Chung-Ming Chen,et al.  Automatic segmentation of liver PET images , 2008, Comput. Medical Imaging Graph..

[5]  Dani Lischinski,et al.  A Closed-Form Solution to Natural Image Matting , 2008 .

[6]  Chunming Li,et al.  Split Bregman Method for Minimization of Region-Scalable Fitting Energy for Image Segmentation , 2010, ISVC.

[7]  Xiaodong Wu,et al.  Comparative Study With New Accuracy Metrics for Target Volume Contouring in PET Image Guided Radiation Therapy , 2013, IEEE Transactions on Medical Imaging.

[8]  Hongbin Wang,et al.  Automated MAP-MRF EM labelling for volume determination in PET , 2008, 2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro.

[9]  S M Larson,et al.  Segmentation of lung lesion volume by adaptive positron emission tomography image thresholding , 1997, Cancer.

[10]  Mila Nikolova,et al.  Algorithms for Finding Global Minimizers of Image Segmentation and Denoising Models , 2006, SIAM J. Appl. Math..

[11]  Yuji Nakamoto,et al.  Reproducibility of common semi-quantitative parameters for evaluating lung cancer glucose metabolism with positron emission tomography using 2-deoxy-2-[18F]fluoro-D-glucose. , 2002, Molecular imaging and biology : MIB : the official publication of the Academy of Molecular Imaging.

[12]  Brian O'Sullivan,et al.  Intraobserver and interobserver variability in GTV delineation on FDG-PET-CT images of head and neck cancers. , 2007, International journal of radiation oncology, biology, physics.