Enhancement, Detection, and Visualization of 3D Volume Data

Abstract: Three-dimensional (3D) volume data become increasingly common with the emergence and wide availability of modern 3D image acquisition techniques. The demand for computerized analysis and visualization techniques is constantly growing to utilize the abundant information embedded in these data. This thesis consists of two parts. The first part presents methods of analyzing 3D volume data by using second derivatives. Harmonic functions are used to combine the non-orthogonal second derivative operators into an orthogonal basis. Three basic features, magnitude, shape, and orientation, are extracted from the second derivative responses after diagonalizing the Hessian matrix. Two applications on magnetic resonance angiography (MRA) data are presented. One of them utilizes a scale-space and the second order variation to enhance the vascular system by discriminating for string structures. The other one employs the local shape information to detect cases of stenosis. The second part of the thesis proposes a new frame-coherent volume rendering algorithm. It is an extension of the algorithm by Gudmundsson and Randén (1990). The new algorithm is capable of efficiently generating rotation sequences around an arbitrary axis. Essentially, it enables the ray-casting procedure to quickly approach the hull of the object using the so called shadow-lines recorded from the previous frame.