The measurement of meaning

Medical Image Computing and Computer-Assisted Intervention

Perceptual visual quality metrics: A survey

Current computed tomography (CT) technology allows for near isotropic, submillimeter resolution acquisition of the complete chest in a single breath hold. These thin-slice chest scans have become indispensable in thoracic radiology, but have also substantially increased the data load for radiologists. Automating the analysis of such data is, therefore, a necessity and this has created a rapidly developing research area in medical imaging. This paper presents a review of the literature on computer analysis of the lungs in CT scans and addresses segmentation of various pulmonary structures, registration of chest scans, and applications aimed at detection, classification and quantification of chest abnormalities. In addition, research trends and challenges are identified and directions for future research are discussed.

Computer analysis of computed tomography scans of the lung: a survey

https://cg.informatik.uni-freiburg.de/intern/seminar/surfaceReconstruction_survey%20of%20marching%20cubes.pdf

A survey of the marching cubes algorithm

Categorization of objects solely based on shape and appearance is still a largely unresolved issue. With the advent of new sensor technologies, such as consumer-level range sensors, new possibilities for shape processing have become available for a range of new application domains. In the first part of this paper, we introduce a novel, large dataset containing 18 categories of objects found in typical household and office environments—we envision this dataset to be useful in many applications ranging from robotics to computer vision. The second part of the paper presents computational experiments on object categorization with classifiers exploiting both two-dimensional and three-dimensional information. We evaluate categorization performance for both modalities in separate and combined representations and demonstrate the advantages of using range data for object and shape processing skills.

Going into depth: Evaluating 2D and 3D cues for object classification on a new, large-scale object dataset

Utilizing augmented reality for applications in medicine has been a topic of intense research for several years. A number of challenging tasks need to be addressed when designing a medical AR system. These include the import and management of medical datasets and preoperatively created planning data, the registration of the patient with respect to a global coordinate system, and accurate tracking of the camera used in the AR setup as well as the respective surgical instruments. Most research systems rely on specialized hardware or algorithms for realizing augmented reality in medicine. Such base technologies can be expensive or very time-consuming to implement. In this paper, we propose an alternative approach of building a surgical AR system by harnessing existing, commercially available equipment for image guided surgery (IGS). We describe the prototype of an augmented reality application, which receives all necessary information from a device for intraoperative navigation.

Medical augmented reality based on commercial image guided surgery

Segmentation of the tracheo-bronchial tree of the lungs is notoriously difficult. This is due to the fact that the small size of some of the anatomical structures is subject to partial volume effects. Furthermore, the limited intensity contrast between the participating materials (air, blood, and tissue) increases the segmentation of difficulties. In this paper, we propose a hybrid segmentation method which is based on a pipeline of three segmentation stages to extract the lower airways down to the seventh generation of the bronchi. User interaction is limited to the specification of a seed point inside the easily detectable trachea at the upper end of the lower airways. Similarly, the complementary vascular tree of the lungs can be segmented. Furthermore, we modified our virtual endoscopy system to visualize the vascular and airway system of the lungs along with other features, such as lung tumors.

Hybrid segmentation and exploration of the human lungs

Traditionally, computer graphics strived to achieve the technically best representation of the scenario or scene. For rendering, this lead to the preeminence of representations based on the physics of light interacting with different media and materials. Research in virtual reality has focussed on interactivity and therefore on real-time rendering techniques that improve the immersion of users in the virtual environments. In contrast, visualization has focused on representations that that maximizes the information content. In most cases, such representations are not physically-based, requiring instead more abstract approaches. Recently, the increasing integration of the extensive knowledge and methods from perception research into computer graphics has fundamentally altered both fields, offering not only new research questions, but also new ways of solving existing issues. Against this backdrop of an increasing importance of perceptual research in all areas related to computergenerated imagery, we discuss the state of the art of perception in computer graphics.

The Role of Perception for Computer Graphics

The support of surgical interventions has long been in the focus of application-oriented augmented reality research. Modern methods of surgery, like minimally-invasive procedures, can benefit from the additional information visualization provided by augmented reality. The usability of medical augmented reality depends on a rendering scheme for virtual objects designed to generate easily and quickly understandable augmented views. One important factor for providing such an accessible reality augmentation is the correct handling of the occlusion of virtual objects by real scene elements. The usually large volumetric datasets used in medicine are ill-suited for use as phantom models for static occlusion handling. We present a simple and fast preprocessing pipeline for medical volume datasets which extracts their visual hull volume. The resulting, significantly simplified visual hull iso-surface is used for real-time static occlusion handling in our AR system, which is based on off-the-shelf medical equipment.

Jan Fischer

Papers

Going into depth: Evaluating 2D and 3D cues for object classification on a new, large-scale object dataset

Medical augmented reality based on commercial image guided surgery

Hybrid segmentation and exploration of the human lungs

The Role of Perception for Computer Graphics

Occlusion handling for medical augmented reality using a volumetric phantom model