TY - JOUR
T1 - Using kinetic parameters analysis of dynamic FDOPA-PET for brain tissues classification
AU - Lin, Hong Dun
AU - Lin, Kang Ping
AU - Chung, Being Tau
AU - Yu, Chin Lung
AU - Wang, Rong Fa
AU - Wu, Liang Chih
AU - Liu, Ren Shyan
PY - 2002
Y1 - 2002
N2 - 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 40% and 27% 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.
AB - 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 40% and 27% 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.
KW - Kinetic parametric
KW - PET
KW - Segmentation
UR - http://www.scopus.com/inward/record.url?scp=0036033460&partnerID=8YFLogxK
U2 - 10.1117/12.463611
DO - 10.1117/12.463611
M3 - Conference article
AN - SCOPUS:0036033460
SN - 0277-786X
VL - 4683
SP - 436
EP - 443
JO - Proceedings of SPIE - The International Society for Optical Engineering
JF - Proceedings of SPIE - The International Society for Optical Engineering
T2 - Medical Imaging 2002: Physiology and Function from Multidimensional Images
Y2 - 24 February 2002 through 26 February 2002
ER -