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D. Roselli

dblp:142/4001 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 1993
—ORCID · none

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

Artificial intelligence and machine learning · 1Graphics, 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.

Interdisciplinary, comprehensive, and emerging computing
1 paper
Medical and health informatics · 100%
Computer graphics and multimedia
1 paper
Visualization and visual analytics · 100%

Topics — the 3 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Medical and health informatics › medical imaging › medical image analysis
brain MRI segmentation
0.011993
Pinta: a system for visualizing the anatomical structures of the brain from MR imaging · CVPR 1993
Medical and health informatics › medical imaging
medical image analysis
0.011993
Pinta: a system for visualizing the anatomical structures of the brain from MR imaging · CVPR 1993
Visualization and visual analytics
medical visualization
0.011993
Pinta: a system for visualizing the anatomical structures of the brain from MR imaging · CVPR 1993

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

volumetric consistency · 0.0region grouping · 0.0attributed graph · 0.0
YearPublicationVenuePosition
1993 Pinta: a system for visualizing the anatomical structures of the brain from MR imaging
abstract
Pinta is a system for segmentation and visualization of anatomical structures obtained from serial sections reconstructed from magnetic resonance imaging. The system approaches the segmentation problem by assigning each volumetric region to an anatomical structure. This is accomplished by satisfying constraints at the pixel level, slice level, and volumetric level. Each slice is represented by an attributed graph, where nodes correspond to regions and links correspond to the relations between regions. These regions are obtained by grouping pixels based on similarity and proximity. The slice level attributed graphs are then coerced to form a volumetric attributed graph, where volumetric consistency can be verified. The volumetric graph is used to ensure consistency from symbolic representations obtained from individual slices. The system allows errors to be made at the slice level, yet removes them when the volumetric consistency cannot be verified. Once the segmentation is complete, the 3D surfaces of the brain can be constructed and visualized.>
Bahram Parvin, William E. Johnston, D. Roselli
CVPR3