1. Technical Field
The present invention relates generally to computer-aided diagnosis (CADx) and, in particular, to a computer-aided diagnosis (CADx) method for aiding diagnosis of three-dimensional digital image data.
2. Background Description
Computer-assisted diagnosis is an important technology in many clinical applications. In current clinical practice, cancer or other diseases may be missed during a physician's un-aided examination of medical image data, in part because of the large volume of data. This is particularly a problem for screening applications, since there is generally little time to devote to the examination of each patient's data, and the entire range of the data must be examined to make sure it is free from disease. Computer analysis that is performed silently in the background can greatly aid physicians in their work.
New technologies that offer three-dimensional (3D) scans of the human body, such as Magnetic Resonance Imaging (MRI) and Computed Tomography (CT), offer tremendous opportunities for improved detection of disease. However, the change from two dimensions to three means that there is a much larger amount of data for the physician to examine. Thus, the assistance of computer analysis becomes even more important.
The computer screen can only provide a two-dimensional (2D) view of a three-dimensional data set. There are many different approaches to presenting two-dimensional representations of such data, Traditional volume rendering algorithms sometimes produce satisfactory results, but they require pre-set thresholds to determine what types of tissue will be drawn as transparent and what types will be drawn as opaque. For complex anatomical structures, the volume rendering may be too cluttered for the physician to form a mental model of the underlying three-dimensional structure of the data, and how it corresponds to normal and abnormal anatomical structure. It would be useful for a CADx system to provide a visualization that helps the physician quickly build up the model, and easily distinguish abnormal structures from normal ones.
Unfortunately, many current CADx systems are not readily accepted by physicians, because their aid is seen as more of a distraction than a help. Many such systems present the results of the computer's diagnosis to the physician by marks, such as a red circle or arrow on the softcopy, which some physicians feel can create a bias in their interpretation of the data. Furthermore, many systems are perceived as a “black box”; that is, the physicians feel that they do not have any understanding of how the systems work and how they generate their diagnosis.
It would be more desirable to integrate physician's knowledge into the CADx process seamlessly, and without introducing marks onto the soft-copy that may be distracting or annoying to physicians.