Visualizing what lies inside

• Authors: Carlos D. Correa
• Venue: ACM SIGGRAPH Computer Graphics - Building Bridges - Science, the Arts & Technology
• Year: 2009

Medical imaging has given radiologists an ability that photography was not able to provide: it lets them see inside the human body. With the advent of 3D visualization systems, these images can be put together into crisp and impressive renderings of the human body from a variety of perspectives that were only dreamt of before, revolutionizing clinical practice. Light transport models soon emerged to allow light interactions that, although not realistic in the physical sense, proved to be more effective for understanding the complex relationships among the anatomical structures. For instance, bone could be made semi-transparent to provide visibility of brain tissue. Skin could be removed altogether from an image to show only muscle or internal organs. However, soon it became evident that simply rendering these images in their raw form was no longer effective and the clear visualization of internal structures remains elusive. The depiction of internal parts in the context of the enclosing space is a difficult problem that has occupied the mind of artists, illustrators and visualization practitioners. Despite the advances made in computer graphics for simulating the light transport in semi-transparent media, the problem of visualizing internal objects is no longer a rendering problem, but that of classification. Medical imaging technology obtains representations of anatomical structures via indirect ways, such as the response of tissue to X-rays or the alignment of electrons in a magnetic field. Therefore, the absence of semantic information prevents visualization practitioners from clearly marking up the regions that must be visualized. Without access to those regions, exploration becomes tedious and time-consuming. The predominant approach has been the use of transfer functions, or opacity mappings, which assign transparency properties to different intervals in the data. This method, however, does not guarantee that internal structures are visible. Other strategies must be used. In this article, I describe some visualization techniques that have emerged to obtain clear views of internal features in 3D volume data.