Linked Life Data

12561372

Source:http://linkedlifedata.com/resource/pubmed/id/12561372

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
4
pubmed:dateCreated
2003-2-3
pubmed:abstractText
The compression of medical image is the key to study tele-medicine & PACS. We have studied the statistical distribution of wavelet subimage coefficients and concluded that the distribution of wavelet subimage coefficients is very much similar to that of Laplacian distribution. Based on the statistical properties of image wavelet decomposition, an image quantization algorithm is proposed. In this algorithm, we selected the sample-standard-deviation as the key quantization threshold in every wavelet subimage. The test has proved that, the main advantages of this algorithm are simple computing and the predictability of coefficients in different quantization threshold range. Also, high compression efficiency can be obtained. Therefore, this algorithm can be potentially used in tele-medicine and PACS.
pubmed:language
chi
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:month
Dec
pubmed:issn
1001-5515
pubmed:author
pubmed:issnType
Print
pubmed:volume
19
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
657-9, 675
pubmed:dateRevised
2006-11-15
pubmed:meshHeading
pubmed:year
2002
pubmed:articleTitle
[An algorithm of a wavelet-based medical image quantization].
pubmed:affiliation
Bio-Engineering Institute of Chongqing University, Chongqing 400044.
pubmed:publicationType
Journal Article, English Abstract