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Proceedings Paper

Using kinetic parameter analysis of dynamic FDOPA-PET for brain tissue classification
Author(s): Hong-Dun Lin; Kang-Ping Lin; Being-Tau Chung; Chin-Lung Yu; Rong-Fa Wang; Liang-Chi Wu; Ren-Shyan Liu
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Paper 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.

Paper Details

Date Published: 24 April 2002
PDF: 8 pages
Proc. SPIE 4683, Medical Imaging 2002: Physiology and Function from Multidimensional Images, (24 April 2002); doi: 10.1117/12.463611
Show Author Affiliations
Hong-Dun Lin, Chung-Yuan Christian Univ. (Taiwan)
Kang-Ping Lin, Chung-Yuan Christian Univ. (Taiwan)
Being-Tau Chung, Industrial Technology Research Institute (Taiwan)
Chin-Lung Yu, Taipei Veterans General Hospital (Taiwan)
Rong-Fa Wang, Taipei Veterans General Hospital (Taiwan)
Liang-Chi Wu, Taipei Veterans General Hospital (Taiwan)
Ren-Shyan Liu, Taipei Veterans General Hospital (Taiwan)


Published in SPIE Proceedings Vol. 4683:
Medical Imaging 2002: Physiology and Function from Multidimensional Images
Anne V. Clough; Chin-Tu Chen, Editor(s)

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