TY - GEN
T1 - A family of principal component analyses for dealing with outliers
AU - Iglesias, J. Eugenio
AU - De Bruijne, Marleen
AU - Loog, Marco
AU - Lauze, François
AU - Nielsen, Mads
PY - 2007
Y1 - 2007
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=38349107132&partnerID=8YFLogxK
U2 - 10.1007/978-3-540-75759-7_22
DO - 10.1007/978-3-540-75759-7_22
M3 - Conference proceeding
C2 - 18044567
AN - SCOPUS:38349107132
SN - 9783540757580
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 178
EP - 185
BT - Medical Image Computing and Computer-Assisted Intervention - MICCAI 2007 - 10th International Conference, Proceedings
T2 - 10th International Conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2007
Y2 - 29 October 2007 through 2 November 2007
ER -