Relation-aware spreadsheets for multimodal volume segmentation and visualization

Authors:
Lin Zheng;Yingcai Wu;Kwan-Liu Ma
Affiliations:
Department of Computer Science, The University of California, Davis;Department of Computer Science, The University of California, Davis;Department of Computer Science, The University of California, Davis
Venue:
MLMI'10 Proceedings of the First international conference on Machine learning in medical imaging
Year:
2010

Citing 7
Cited 0

Spreadsheets for images

SIGGRAPH '94 Proceedings of the 21st annual conference on Computer graphics and interactive techniques
Qualitative Spatial Representation and Reasoning with the Region Connection Calculus

Geoinformatica
Visualization Exploration and Encapsulation via a Spreadsheet-Like Interface

IEEE Transactions on Visualization and Computer Graphics
Principles for Information Visualization Spreadsheets

IEEE Computer Graphics and Applications
Scene Graphs in the New Millennium

IEEE Computer Graphics and Applications
Local Histograms for Design of Transfer Functions in Direct Volume Rendering

IEEE Transactions on Visualization and Computer Graphics
Relation-Aware Volume Exploration Pipeline

IEEE Transactions on Visualization and Computer Graphics

Quantified Score

Hi-index	0.00

Visualization

Abstract

Multimodal volume data commonly found in medical imaging applications present both opportunities and challenges to segmentation and visualization tasks. This paper presents a user directed volume segmentation system. Through a spreadsheets interface, the user can interactively examine and refine segmentation results obtained from automatic clustering. In addition, the user can isolate or highlight a feature of interest in a volume based on different modalities, and see the corresponding segmented results. Our system is easy to use since the preliminary segmentation results are organized and presented to the user in a relation-aware fashion based on the spatial relations between the segmented regions. We demonstrate this system using two multimodal datasets.