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Ronald M. McLaughlin

dblp:80/7527 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 2009
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Graphics, computer vision, multimedia, augmented reality and games · 1

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Computer graphics and multimedia
1 paper
Rendering · 61% Visualization and visual analytics · 30% Computational photography and imaging · 9%

Topics — the 4 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Visualization and visual analytics
biomedical visualization
0.112009
Volume Illustration of Muscle from Diffusion Tensor Images · IEEE Trans. Vis. Comput. Graph. 2009
Rendering › volume rendering
volume illustration
0.112009
Volume Illustration of Muscle from Diffusion Tensor Images · IEEE Trans. Vis. Comput. Graph. 2009
Rendering
volume rendering
0.112009
Volume Illustration of Muscle from Diffusion Tensor Images · IEEE Trans. Vis. Comput. Graph. 2009
Computational photography and imaging
diffusion tensor imaging
0.012009
Volume Illustration of Muscle from Diffusion Tensor Images · IEEE Trans. Vis. Comput. Graph. 2009

Methods — techniques the papers use, named apart from their topics

solid texture synthesis · 0.1guidance vector field · 0.1example-based texture synthesis · 0.1
YearPublicationVenuePosition
2009 Volume Illustration of Muscle from Diffusion Tensor Images
abstract
Medical illustration has demonstrated its effectiveness to depict salient anatomical features while hiding the irrelevant details. Current solutions are ineffective for visualizing fibrous structures such as muscle, because typical datasets (CT or MRI) do not contain directional details. In this paper, we introduce a new muscle illustration approach that leverages diffusion tensor imaging (DTI) data and example-based texture synthesis techniques. Beginning with a volumetric diffusion tensor image, we reformulate it into a scalar field and an auxiliary guidance vector field to represent the structure and orientation of a muscle bundle. A muscle mask derived from the input diffusion tensor image is used to classify the muscle structure. The guidance vector field is further refined to remove noise and clarify structure. To simulate the internal appearance of the muscle, we propose a new two-dimensional example based solid texture synthesis algorithm that builds a solid texture constrained by the guidance vector field. Illustrating the constructed scalar field and solid texture efficiently highlights the global appearance of the muscle as well as the local shape and structure of the muscle fibers in an illustrative fashion. We have applied the proposed approach to five example datasets (four pig hearts and a pig leg), demonstrating plausible illustration and expressiveness.
Wei Chen 0001, Zhicheng Yan 0001, Song Zhang 0004, John Allen Crow, David S. Ebert, Ronald M. McLaughlin, Katie B. Mullins, Robert Cooper, Zi'ang Ding
IEEE Trans. Vis. Comput. Graph.6