DocumentCode :
2459249
Title :
2D-PCA Based Statistical Shape Model from few Medical Samples
Author :
Tateyama, Tomoko ; Foruzan, Hossein ; Chen, Yen-wei
Author_Institution :
Coll. of Inf. Sci. & Eng., Ritsumeikan Univ., Kusatsu, Japan
fYear :
2009
fDate :
12-14 Sept. 2009
Firstpage :
1266
Lastpage :
1269
Abstract :
Statistical shape model (SSM) is to model the shape variation of an object. In this paper, we propose an efficient shape representation method and a new 2D-PCA based statistical shape modeling. In our proposed method, we used the radii of these surface points as shape feature instead of their coordinates, and the shape is represented by a 2D matrices. We then apply 2D-PCA to construct a statistical shape model with generalization even from fewer samples.
Keywords :
matrix algebra; medical image processing; principal component analysis; 2D PCA; 2D matrices; generalization; object shape variation; principal component analysis; shape feature; shape representation method; statistical shape modeling; Biomedical engineering; Computed tomography; Educational institutions; Feature extraction; Image segmentation; Information analysis; Information science; Liver; Shape; Signal processing;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Intelligent Information Hiding and Multimedia Signal Processing, 2009. IIH-MSP '09. Fifth International Conference on
Conference_Location :
Kyoto
Print_ISBN :
978-1-4244-4717-6
Electronic_ISBN :
978-0-7695-3762-7
Type :
conf
DOI :
10.1109/IIH-MSP.2009.246
Filename :
5337215
Link To Document :
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