Automatic extraction of abnormal signals from diffusion-weighted images using 3D-ACTIT

Recent developments in medical imaging equipment have made it possible to acquire large amounts of image data and to perform detailed diagnosis. However, it is difficult for physicians to evaluate all of the image data obtained. To address this problem, computer-aided detection (CAD) and expert systems have been investigated. In these investigations, as the types of images used for diagnosis has expanded, the requirements for image processing have become more complex. We therefore propose a new method which we call Automatic Construction of Tree-structural Image Transformation (3D-ACTIT) to perform various 3D image processing procedures automatically using instance-based learning. We have conducted research on diffusion-weighted image (DWI) data and its processing. In this report, we describe how 3D-ACTIT performs processing to extract only abnormal signal regions from 3D-DWI data. (author)