Alveolar nerve unfolding technique for synoptic analysis: visualization and quantification of inferior alveolar nerve tracings in three-dimensional cone-beam computed tomography.

The aim of the technique presented here is to visualize the anatomical context of the inferior alveolar nerve (IAN) canal. For 2 cases, cone-beam computed tomography images of the mandible were obtained from patient files together with the manual preoperative IAN canal tracings. For both cases, similar to simulated panoramic images, a two-dimensional image is extracted from a three-dimensional cone-beam computed tomography image.

On the augmented reproducibility in measurements on 3D orthodontic digital dental models and the definition of feature points.

Objective: The objective of this study was to explore digital measurement methodology on 3-dimensional (3D) dental models. Standardised manipulation and practices have lead to reliable measurements on plaster casts. Identifying landmarks on digital models or digitised plaster casts is fundamentally different from actual measurements.

Vascular active contour for vessel tree segmentation.

In this paper, a novel active contour model is proposed for vessel tree segmentation. First, we introduce a region competition-based active contour model exploiting the gaussian mixture model, which mainly segments thick vessels. Second, we define a vascular vector field to evolve the active contour along its center line into the thin and weak vessels.

Novel information theory based method for superimposition of lateral head radiographs and cone beam computed tomography images.

Objectives: The aim was to introduce a novel alignment criterion, focus mutual information (FMI), for the superimposition of lateral cephalometric radiographs and three dimensional (3D) cone beam computed images as well as the assessment of the alignment characteristics of the new method and comparison of the novel methodology with the region of interest (ROI) approach.

Methods: Implementation of a FMI criterion-based methodology that only requires the approximate indication of stable structures in one single image. The robustness of the method was first addressed in a phantom experiment comparing the new technique with a ROI approach.

2D image registration using focused mutual information for application in dentistry.

Spatial alignment of image data is a common task in computer vision and medical imaging. This should preferentially be done with minimal intervention of an operator. Similarity measures with origin in the information theory such as mutual information (MI) have proven to be robust registration criteria for this purpose.