Abstract
We propose a novel Statistical Piecewise Assembled Model (SPAM) to address the open problem of small sample size encountered when applying Point Distribution Models (PDM) in 3-D medical data analysis. Specifically, in our SPAM, the Statistical Frame Model (SFM) constructed from the salient landmarks characterizes the global topological variability of the structure. Then the landmarks are employed to partition a complex object surface into piecewise segments. After that, the Statistical deformable Piecewise surface segment Models (SPMs) are established to define the fine details of local surface shape variations. The hierarchical nature of SPAM enables it to generate much more variation modes than conventional statistical models given a very small sample size training set. The experimental results demonstrate that SPAM can achieve more accuracy rates for model representation compared with traditional Active Shape Model (ASM) and Multi-resolution ASM.
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© 2008 Springer-Verlag Berlin Heidelberg
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Feng, J., Du, P., Ip, H.H.S. (2008). Statistical Piecewise Assembled Model (SPAM) for the Representation of Highly Deformable Medical Organs. In: Dohi, T., Sakuma, I., Liao, H. (eds) Medical Imaging and Augmented Reality. MIAR 2008. Lecture Notes in Computer Science, vol 5128. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-79982-5_19
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DOI: https://doi.org/10.1007/978-3-540-79982-5_19
Publisher Name: Springer, Berlin, Heidelberg
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