Curvature- and Model-Based Surface Hatching of Anatomical Structures Derived from Clinical Volume Datasets

Computer Graphics Forum

et al. 2018

Self Cite

In this paper, we survey illustrative rendering techniques for 3D surface models. We first discuss the field of illustrative visualization in general and provide a new definition for this sub‐area of visualization. For the remainder of the survey, we then focus on surface‐based models. We start by briefly summarizing the differential geometry fundamental to many approaches and discuss additional general requirements for the underlying models and the methods' implementations. We then provide an overview of low‐level illustrative rendering techniques including sparse lines, stippling and hatching, and illustrative shading, connecting each of them to practical examples of visualization applications. We also mention evaluation approaches and list various application fields, before we close with a discussion of the state of the art and future work.

Section: Low‐level Illustrative Visualization Techniquesmentioning

confidence: 99%

A Survey of Surface‐Based Illustrative Rendering for Visualization

Lawonn

Viola

Computer Graphics Forum

et al. 2018

Self Cite

“…As an example, we presented smart hatching techniques for muscles and vascular structures (see Fig. 6) [5]. However, in case of hatching there has been no substantial evaluation with medical doctors.…”

Section: Smart Illustrative Visualizationsmentioning

confidence: 99%

Smart Graphics in Medical VisualizationSmart Graphics in Medizinischen Visualisierungen

Muehler¹,

It - Information Technology

2009

Self Cite

Smart graphics techniques have a great potential in supporting medical treatment planning and education. In particular, the use of high-level knowledge and carefully selected default values reduces the interaction effort in exploring complex 3D data sets. Automatic viewpoint selection techniques may support the exploration of patient-specific 3D models. Camera movements and other animation techniques allow to emphasize important anatomic structures and convey complex spatial relations. Illustrative rendering techniques and smart visibility techniques enable the expressive visualization of nested surfaces which is important, e. g., to understand the location of a tumor inside an organ. General smart graphics techniques, however, have to be carefully adapted to support medical applications. In-depth discussions of usage scenarios, requirements, priorities and preferences are therefore a prerequisite for successful developments. In this paper, we give an overview of smart medical visualizations.

“…These areas and the shading are used to define the hatching styles. Praun et al [PHWF01] introduced a real‐time hatching method which builds a lapped texture parametrization over the mesh which are aligned to a curvature‐based direction field Gasteiger et al [GTBP08] presented a texture‐based method to hatch anatomical structures. Hummel et al [HGH*10] examined the use of transparancy and texturing techniques to illustrate vector fields.…”

Section: Related Workmentioning

confidence: 99%

Streamlines for Illustrative Real‐Time Rendering

Lawonn

Moench

Computer Graphics Forum

2013

Line drawing techniques are important methods to illustrate shapes. Existing feature line methods, e.g., suggestive contours, apparent ridges, or photic extremum lines, solely determine salient regions and illustrate them with separate lines. Hatching methods convey the shape by drawing a wealth of lines on the whole surface. Both approaches are often not sufficient for a faithful visualization of organic surface models, e.g., in biology or medicine. In this paper, we present a novel object-space line drawing algorithm that conveys the shape of such surface models in real-time. Our approach employs contour-and feature-based illustrative streamlines to convey surface shape (ConFIS). For every triangle, precise streamlines are calculated on the surface with a given curvature vector field. Salient regions are detected by determining maxima and minima of a scalar field. Compared with existing feature lines and hatching methods, ConFIS uses the advantages of both categories in an effective and flexible manner. We demonstrate this with different anatomical and artificial surface models. In addition, we conducted a qualitative evaluation of our technique to compare our results with exemplary feature line and hatching methods.