We present a new algorithm, called marching cubes, that creates triangle models of constant density surfaces from 3D medical data. Using a divide-and-conquer approach to generate inter-slice connectivity, we create a case table that defines triangle topology. The algorithm processes the 3D medical data in scan-line order and calculates triangle vertices using linear interpolation. We find the gradient of the original data, normalize it, and use it as a basis for shading the models. The detail in images produced from the generated surface models is the result of maintaining the inter-slice connectivity, surface data, and gradient information present in the original 3D data. Results from computed tomography (CT), magnetic resonance (MR), and single-photon emission computed tomography (SPECT) illustrate the quality and functionality of marching cubes. We also discuss improvements that decrease processing time and add solid modeling capabilities.

For applications of volume visualization in medicine, it is important to assure that the 3D images show the true anatomical situation, or at least to know about their limitations. In this paper, various methods for evaluation of image quality are reviewed. They are classified based on the fundamental terms of intelligibility and fidelity, and discussed with respect to the question what clues they provide on how to choose parameters, or improve imaging and visualization procedures.