國家衛生研究院 NHRI:Item 3990099045/4850
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    题名: A unified segmentation method for CT image segmentation with contrast agent
    其它题名: 2006 IEEE Nuclear Science Symposium, Medical Imaging Conference and 15th International Workshop on Room-Temperature Semiconductor X- and Gamma-Ray Detectors, Special Focus Workshops, NSS/MIC/RTSD
    作者: Lee, HF;Huang, PC;Wietholt, C;Hsu, CH;Lin, KM;Chen, CT;Hsiao, IT;Yen, TC;Jan, ML
    贡献者: Division of Medical Engineering Research
    摘要: An important synergy of PET/CT scanners is the use of the CT data for X-ray based attenuation correction of the PET emission data. The attenuation map of PET data can be estimated by partitioning CT image into different tissue types and then assigning the corresponding attenuation coefficients of tissues at 511 keV. The accuracy of attenuation map depends on the tissue classification of CT image. In this paper, we propose a unified segmentation method of CT images with/without contrast agent in PET/CT imaging. The tissue types of CT images can be classified as bone, lung, air, and soft tissue without and with contrast agent. A mixed fuzzy c-mean clustering algorithm is developed to perform automatic segmentation. The performance of the proposed unified segmentation method was evaluated by using clinical CT images. Preliminary experimental results show that the proposed method can successfully separate different tissue types, particularly soft tissue with contrast agent and bone, compared to the segmented results obtained by using the simple intensity-based fuzzy c-means algorithms. We anticipate that the proposed segmentation method is an effective way to obtain CT-based attenuation map for clinical PET/CT quantification. .
    日期: 2007-11
    關聯: 2006 IEEE Nuclear Science Symposium Conference Record.2006 Nov;5:2632-2635(Article number 4179580).
    Link to: http://dx.doi.org/10.1109/NSSMIC.2006.356423
    Cited Times(WOS): https://www.webofscience.com/wos/woscc/full-record/WOS:000288875602149
    Cited Times(Scopus): http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=38549092543
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