Statistical Texture Modeling for Medical Volume Using Generalized N-Dimensional Principal Component Analysis Method and 3D Volume Morphing

In this paper, a statistical texture modeling method is proposed for medical volumes. As the shapes of the human organ are very different from one case to another, 3D volume morphing is applied to normalize all the volume datasets to a same shape for removing shape variations. In order to deal with the problems of high-dimension and small number of medial samples, we propose an effective image compression method named Generalized N-dimensional Principal Component Analysis (GND-PCA) to construct a statistical model. Experiments applied on liver volumes show good performance on generalization using our method. A simple experiment is employed to show that the features extracted by the statistical texture model have capability of discrimination for different types of data, such as normal and abnormal.