抄録
In the field of medical image processing, 3-D (three dimensional) image processing is often required to deal with MRI, CT and Positron Emission Tomography (PET) image data. However, it is not easy to construct complex 3D processing procedures manually compared with 2D ones. Thus, we previously proposed a new method named 3-D Automatic Construction of Tree-structural Image Transformation (3D-ACTIT) for making various 3-D image-processing procedures based upon example learning. In this paper, we apply 3D-ACTIT to 3-D PET image data. PET is a kind of medical image which indicates the metabolism of the human body. This type of image has been used recently as a method of detecting cancer, but because of low resolution and unclear indication, it is difficult to distinguish the outline of internal organs. Experimental results show that liver region segmentation from 3D-PET image data can be constructed automatically by the proposal method.
