Using kinetic parameter analysis of dynamic FDOPA-PET for brain tissue classification

Hong-Dun Lin; Kang-Ping Lin; Being-Tau Chung; Chin-Lung Yu; Rong-Fa Wang; Liang-Chi Wu; Ren-Shyan Liu

doi:10.1117/12.463611

24 April 2002 Using kinetic parameter analysis of dynamic FDOPA-PET for brain tissue classification

Hong-Dun Lin, Kang-Ping Lin, Being-Tau Chung, Chin-Lung Yu, Rong-Fa Wang, Liang-Chi Wu, Ren-Shyan Liu

Proceedings Volume 4683, Medical Imaging 2002: Physiology and Function from Multidimensional Images; (2002) https://doi.org/10.1117/12.463611
Event: Medical Imaging 2002, 2002, San Diego, California, United States

Abstract

In clinically, structural image based brain tissue segmentation as a preprocess plays an important and essential role on a number of image preprocessing, such as image visualization, object recognition, image registration, and so forth. However, when we need to classify the tissues according to their physiological functions, those strategies are not satisfactory. In this study, we incorporated both tissue time-activity curves (TACs) and derived kinetic parametric curves (KPCs) information to segment brain tissues, such as striatum, gray and white matters, in dynamic FDOPA-PET studies. Four common clustering techniques, K-mean (KM), Fuzzy C-mean (FCM), Isodata (ISO), Markov Random Fields (MRF), and our method were compared to evaluate its precision. The results show 41% and 48% less mean errors in mean difference for KPCs and TACs, respectively, than other methods. Combined KPCs and TACs based clustering method provide the ability to define brain structure effectively.

Citation Download Citation

Hong-Dun Lin, Kang-Ping Lin, Being-Tau Chung, Chin-Lung Yu, Rong-Fa Wang, Liang-Chi Wu, and Ren-Shyan Liu "Using kinetic parameter analysis of dynamic FDOPA-PET for brain tissue classification", Proc. SPIE 4683, Medical Imaging 2002: Physiology and Function from Multidimensional Images, (24 April 2002); https://doi.org/10.1117/12.463611

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