TY - GEN
T1 - Parametric representation of cortical surface folding based on polynomials
AU - Zhang, Tuo
AU - Guo, Lei
AU - Li, Gang
AU - Nie, Jingxin
AU - Liu, Tianming
PY - 2009
Y1 - 2009
N2 - The development of folding descriptors as an effective approach for describing geometrical complexity and variation of the human cerebral cortex has been of great interests. This paper presents a parametric representation of cortical surface patches using polynomials, that is, the primitive cortical patch is compactly and effectively described by four parametric coefficients. By this parametric representation, the patterns of cortical patches can be classified by either model-driven approach or data-driven clustering approach. In the model-driven approach, any patch of the cortical surface is classified into one of eight primitive shape patterns including peak, pit, ridge, valley, saddle ridge, saddle valley, flat and inflection, corresponding to eight sub-spaces of the four parameters. The major advantage of this polynomial representation of cortical folding pattern is its compactness and effectiveness, while being rich in shape information. We have applied this parametric representation for segmentation of cortical surface and promising results are obtained.
AB - The development of folding descriptors as an effective approach for describing geometrical complexity and variation of the human cerebral cortex has been of great interests. This paper presents a parametric representation of cortical surface patches using polynomials, that is, the primitive cortical patch is compactly and effectively described by four parametric coefficients. By this parametric representation, the patterns of cortical patches can be classified by either model-driven approach or data-driven clustering approach. In the model-driven approach, any patch of the cortical surface is classified into one of eight primitive shape patterns including peak, pit, ridge, valley, saddle ridge, saddle valley, flat and inflection, corresponding to eight sub-spaces of the four parameters. The major advantage of this polynomial representation of cortical folding pattern is its compactness and effectiveness, while being rich in shape information. We have applied this parametric representation for segmentation of cortical surface and promising results are obtained.
UR - http://www.scopus.com/inward/record.url?scp=84883836888&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-04271-3_23
DO - 10.1007/978-3-642-04271-3_23
M3 - 会议稿件
C2 - 20426111
AN - SCOPUS:84883836888
SN - 3642042708
SN - 9783642042706
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 184
EP - 191
BT - Medical Image Computing and Computer-Assisted Intervention - MICCAI2009 - 12th International Conference, Proceedings
T2 - 12th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2009
Y2 - 20 September 2009 through 24 September 2009
ER -