Most image processing and visualization applications allow users to configure computation parameters and manipulate the resulting visualizations. SCIRun, VolView, MeVisLab, and the Medical Interaction Toolkit (MITK) are four image processing and visualization frameworks that were built for these purposes. All frameworks are freely available and all allow the use of the ITK C++ library. In this paper, the benefits and limitations of each visualization framework are presented to aid both application developers and users in the decision of which framework may be best to use for their application. The analysis is based on more than 50 evaluation criteria, functionalities, and example applications. We report implementation times for various steps in the creation of a reference application in each of the compared frameworks. The data-flow programming frameworks, SCIRun and MeVisLab, were determined to be best for developing application prototypes, while VolView was advantageous for nonautomatic end-user applications based on existing ITK functionalities, and MITK was preferable for automated end-user applications that might include new ITK classes specifically designed for the application.

In this paper, we introduce a concise and concrete definition of an accurate colon centerline and provide an efficient automatic means to extract the centerline and its associated branches (caused by a forceful touching of colon and small bowel or a deep fold in twisted colon lumen). We further discuss its applications on fly-through path planning and endoscopic simulation, as well as its potential to solve the challenging touching and colon collapse problems in virtual colonoscopy. Experimental results demonstrated its centeredness, robustness, and efficiency.

Introduces a refined general definition of a skeleton that is based on a penalized distance function and that cannot create any of the degenerate cases of the earlier CEASAR (Center-line Extraction Algorithm-Smooth, Accurate and Robust) and TEASAR (Tree-structure Extraction Algorithm for Skeletons-Accurate and Robust) algorithms. Additionally, we provide an algorithm that finds the skeleton accurately and rapidly. Our solution is fully automatic, which frees the user from having to engage in manual data pre-processing. We present the accurate skeletons computed on a number of test data sets. The algorithm is very efficient, as demonstrated by the running times, which were all below seven minutes.

We introduce the TEASAR algorithm which is a treestructure extraction algorithm delivering skeletons that are accurate and robust. Volumetric skeletons are needed for accurate measurements of length along branching and winding structures. Skeletons are also required in automatic virtual navigation, such as traveling through human organs (e.g., the colon) to control movement and orientation of the virtual camera. We introduce a concise but general definition of a skeleton, and provide an algorithm that finds the skeleton accurately and rapidly. Our solution is fully automatic, which frees the user from having to engage in data preprocessing. We present the accurate skeletons computed on a number of test datasets. The algorithm is efficient as demonstrated by the running times on a single 194 MHz MIPS R10000 CPU which were all below five minutes.

We present CEASAR, a centerline extraction algorithm that delivers smooth, accurate and robust results. Centerlines are needed for accurate measurements of length along winding tubular structures. Centerlines are also required in automatic virtual navigation through human organs, such as the colon or the aorta, as they are used to control movement and orientation of the virtual camera. We introduce a concise but general definition of a centerline, and provide an algorithm that finds the centerline accurately and rapidly. Our algorithm is provably correct for general geometries. Our solution is fully automatic, which frees the user from having to engage in data preprocessing. For a number of test datasets, we show the smooth and accurate centerlines computed by our CEASAR algorithm on a single 194 MHz MIPS R10000 CPU within five minutes.