1999
DOI: 10.1109/4233.788585
|View full text |Cite
|
Sign up to set email alerts
|

Information technology applications in biomedical functional imaging

Abstract: Abstract-In parallel with rapid advances in computer technology, biomedical functional imaging is having an ever-increasing impact on healthcare. Functional imaging allows us to see dynamic processes quantitatively in the living human body. However, as we need to deal with four-dimensional time-varying images, space requirements and computational complexity are extremely high. This makes information management, processing, and communication difficult. Using the minimum amount of data to represent the required … Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
1

Citation Types

0
1
0

Year Published

2008
2008
2020
2020

Publication Types

Select...
5

Relationship

0
5

Authors

Journals

citations
Cited by 7 publications
(1 citation statement)
references
References 36 publications
0
1
0
Order By: Relevance
“…Unlike those anatomical images, functional/molecular images such as PET and SPECT allow the in vivo study of physiological and biochemical processes, providing functional information previously not available. This is what most distinguishes medical images from general images [86,210,211]. Physiological function can be estimated at the molecular level by observing the behavior of a small quantity of an administered substance tagged with radioactive atoms.…”
Section: Content-based Medical Imagementioning
confidence: 99%
“…Unlike those anatomical images, functional/molecular images such as PET and SPECT allow the in vivo study of physiological and biochemical processes, providing functional information previously not available. This is what most distinguishes medical images from general images [86,210,211]. Physiological function can be estimated at the molecular level by observing the behavior of a small quantity of an administered substance tagged with radioactive atoms.…”
Section: Content-based Medical Imagementioning
confidence: 99%