Fast Medial Axis Extraction Algorithm on Tubular Large 3D Data by Randomized Erosion

Evaluations on the Applicability of Generic and Modular Image Processing Chains for Quantitative 3D Data Analysis in Clinical Research and Radiographer Training

“…Processed case studies included the following imaging modalities, diagnostic questions and affected anatomies among others: Fully automated segmentation of liver parenchyma requires highly sophisticated methods (Zwettler, Backfrieder, Swoboda, & Pfeifer 2009;Bourquain, Schenk, Link, Preim, Prause, & Peitgen 2002) to achieve results comparable to the results of the semi-automated student segmentations. With presented method, all bone structures, organs and vessels visible from abdominal CT data can be segmented and graphically presented within 30min for a trained user, see Figure 13.…”

“…Segmentations can be applied to the task of surgery planning, e.g. liver lobe resection (Zwettler, Backfrieder, Swoboda, & Pfeifer, 2009). The complementary information from different imaging modalities allows for differential diagnosis in various contexts, e.g.…”

A novel procedure for medial axis reconstruction of vessels from Medical Imaging segmentation

“…A modern approach to the diagnostic problem intends to automate the processing pathway, employing software tools capable of characterising the morphology of the anatomical region, and identifying parameters that can lead to the recognition of pathological alterations. In the biomedical field, in particular, this type of procedure is widely used in the three-dimensional analysis of multiple anatomical regions; one example concerns the identification of lobes in the liver area [ 1 ] and the study of cranial and optic nerves [ 2 , 3 ]. Not infrequently, it also makes it possible to discriminate between different diagnoses, accurately describing the structure of a particular anatomical anomaly [ 4 ].…”

Automated Domain-Specific Feature Selection for Classification-based Segmentation of Tomographic Medical Image Data

“…If all voxels of an anatomical structure are labelled, measurements on the extent and volume become feasible, for instance facilitating the monitoring of the disease progression. Furthermore, from available segmentations 3D surface models can be derived that can be utilized for surgery planning (Zwettler, Backfrieder, Swoboda, & Pfeifer, 2009) or surgical training (Fürst & Schrempf, 2012). Thereby the user interaction and subsequent analysis can be performed on the computer model or utilizing a virtual reality environment, enriched by haptic patient models that are derived from the anatomical segmentations and produced via emerging 3D printing devices.…”