TY - GEN
T1 - IVAR
T2 - 5th IEEE International Conference on Big Data, Big Data 2017
AU - Yu, Lina
AU - Jiang, Hengle
AU - Yu, Hongfeng
AU - Zhang, Chi
AU - McAllister, Josiah
AU - Zheng, Dandan
N1 - Funding Information:
ACKNOWLEDGMENT This research has been sponsored by the University of Nebraska Medical Center (UNMC) Faculty Diversity Fund.
Publisher Copyright:
© 2017 IEEE.
PY - 2017/7/1
Y1 - 2017/7/1
N2 - Medical imaging enables researchers and practitioner to uncover the characteristics of diseases (e.g., human cancer) in great detail. However, the sheer size of resulting imaging data and the high dimension of derived features become a major challenge in data analysis, diagnosis, and knowledge discovery. We present a novel visual analytics system, named iVAR, targeted at observing the comprehensive quantification of tumor phenotypes by effectively exploring a large number of quantitative image features. Our system is comprised of multiple linked views combining visualization of three-dimensional volumes and tumors reconstructed by computed tomography (CT) images, and a radiomic analysis of high-dimensional features quantifying tumor image intensity, shape and texture, and three non-image clinical features. Thus, it offers insights into the overall distribution of quantitative imaging features and also enables detailed analysis of the relationship between features. We demonstrate our system through use case scenarios on a real-world large-scale CT dataset with lung cancer.
AB - Medical imaging enables researchers and practitioner to uncover the characteristics of diseases (e.g., human cancer) in great detail. However, the sheer size of resulting imaging data and the high dimension of derived features become a major challenge in data analysis, diagnosis, and knowledge discovery. We present a novel visual analytics system, named iVAR, targeted at observing the comprehensive quantification of tumor phenotypes by effectively exploring a large number of quantitative image features. Our system is comprised of multiple linked views combining visualization of three-dimensional volumes and tumors reconstructed by computed tomography (CT) images, and a radiomic analysis of high-dimensional features quantifying tumor image intensity, shape and texture, and three non-image clinical features. Thus, it offers insights into the overall distribution of quantitative imaging features and also enables detailed analysis of the relationship between features. We demonstrate our system through use case scenarios on a real-world large-scale CT dataset with lung cancer.
KW - high-dimensional data
KW - interactivity
KW - medical images
KW - radiomics
KW - visual analytics
UR - http://www.scopus.com/inward/record.url?scp=85047726573&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85047726573&partnerID=8YFLogxK
U2 - 10.1109/BigData.2017.8258398
DO - 10.1109/BigData.2017.8258398
M3 - Conference contribution
AN - SCOPUS:85047726573
T3 - Proceedings - 2017 IEEE International Conference on Big Data, Big Data 2017
SP - 3916
EP - 3923
BT - Proceedings - 2017 IEEE International Conference on Big Data, Big Data 2017
A2 - Nie, Jian-Yun
A2 - Obradovic, Zoran
A2 - Suzumura, Toyotaro
A2 - Ghosh, Rumi
A2 - Nambiar, Raghunath
A2 - Wang, Chonggang
A2 - Zang, Hui
A2 - Baeza-Yates, Ricardo
A2 - Baeza-Yates, Ricardo
A2 - Hu, Xiaohua
A2 - Kepner, Jeremy
A2 - Cuzzocrea, Alfredo
A2 - Tang, Jian
A2 - Toyoda, Masashi
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 11 December 2017 through 14 December 2017
ER -