Linked Life Data

20562038

Source:http://linkedlifedata.com/resource/pubmed/id/20562038

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
10
pubmed:dateCreated
2010-9-24
pubmed:abstractText
We present a novel 3-D scalable compression method for medical images with optimized volume of interest (VOI) coding. The method is presented within the framework of interactive telemedicine applications, where different remote clients may access the compressed 3-D medical imaging data stored on a central server and request the transmission of different VOIs from an initial lossy to a final lossless representation. The method employs the 3-D integer wavelet transform and a modified EBCOT with 3-D contexts to create a scalable bit-stream. Optimized VOI coding is attained by an optimization technique that reorders the output bit-stream after encoding, so that those bits belonging to a VOI are decoded at the highest quality possible at any bit-rate, while allowing for the decoding of background information with peripherally increasing quality around the VOI. The bit-stream reordering procedure is based on a weighting model that incorporates the position of the VOI and the mean energy of the wavelet coefficients. The background information with peripherally increasing quality around the VOI allows for placement of the VOI into the context of the 3-D image. Performance evaluations based on real 3-D medical imaging data showed that the proposed method achieves a higher reconstruction quality, in terms of the peak signal-to-noise ratio, than that achieved by 3D-JPEG2000 with VOI coding, when using the MAXSHIFT and general scaling-based methods.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:month
Oct
pubmed:issn
1558-0062
pubmed:author
pubmed:issnType
Electronic
pubmed:volume
29
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
1808-20
pubmed:meshHeading
pubmed:year
2010
pubmed:articleTitle
3-D scalable medical image compression with optimized volume of interest coding.
pubmed:affiliation
Department of Electrical and Computer Engineering, The University of British Columbia, Vancouver, BC V6T1Z4, Canada. victors@ece.ubc.ca
pubmed:publicationType
Journal Article, Research Support, Non-U.S. Gov't