A statistical texture model of the liver based on generalized N-dimensional principal component analysis (GND-PCA) and 3D shape normalization

Authors:
Xu Qiao;Yen-Wei Chen
Affiliations:
College of Information Science and Engineering, Ritsumeikan University, Kusatsu, Japan;College of Information Science and Engineering, Ritsumeikan University, Kusatsu, Japan
Venue:
Journal of Biomedical Imaging - Special issue on Machine Learning in Medical Imaging
Year:
2011

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Abstract

We present a method based on generalized N-dimensional principal component analysis (GND-PCA) and a 3D shape normalization technique for statistical texture modeling of the liver. The 3D shape normalization technique is used for normalizing liver shapes in order to remove the liver shape variability and capture pure texture variations. The GND-PCA is used to overcome overfitting problems when the training samples are too much fewer than the dimension of the data. The preliminary results of leave-one-out experiments show that the statistical texture model of the liver built by our method can represent an untrained liver volume well, even though the mode is trained by fewer samples. We also demonstrate its potential application to classification of normal and abnormal (with tumors) livers.