Abstract
Principal Component Analysis (PCA) has been widely used for dimensionality reduction in shape and appearance modeling. There have been several attempts of making PCA robust against outliers. However, there are cases in which a small subset of samples may appear as outliers and still correspond to plausible data. The example of shapes corresponding to fractures when building a vertebra shape model is addressed in this study. In this case, the modeling of “outliers” is important, and it might be desirable not only not to disregard them, but even to enhance their importance.
A variation on PCA that deals naturally with the importance of outliers is presented in this paper. The technique is utilized for building a shape model of a vertebra, aiming at segmenting the spine out of lateral X-ray images. The results show that the algorithm can implement both an outlier-enhancing and a robust PCA. The former improves the segmentation performance in fractured vertebrae, while the latter does so in the unfractured ones.
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Keywords
- Principal Component Analysis
- Reconstruction Error
- Shape Model
- Active Shape Model
- Robust Principal Component Analysis
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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Iglesias, J.E., de Bruijne, M., Loog, M., Lauze, F., Nielsen, M. (2007). A Family of Principal Component Analyses for Dealing with Outliers. In: Ayache, N., Ourselin, S., Maeder, A. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2007. MICCAI 2007. Lecture Notes in Computer Science, vol 4792. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-75759-7_22
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DOI: https://doi.org/10.1007/978-3-540-75759-7_22
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