Linked Life Data

19268385

Source:http://linkedlifedata.com/resource/pubmed/id/19268385

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
1
pubmed:dateCreated
2009-4-27
pubmed:abstractText
The automatic identification and classification of spermatogonium images is a very important issue in biomedical engineering research. This paper proposes a scheme for spermatogonium recognition, in which Zernike moments are used to represent image features. First of all, the mathematical morphology method is employed to extract the intact individual cell in every image, and then we normalize these binary images. Then, Zernike moments are calculated from these normalized images, followed by recognizing the spermatogonia through computing similarity of vectors composed with Zernike moments using Euclidean distance. Experimental results demonstrate that the proposed method, based on Zernike moments, outperforms two well-known methods, namely those based on Hu moments and boundary moments. This method has stronger distinguishing ability, showing better performance in discriminating cell images whether belong to the same cell.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:month
Jul
pubmed:issn
1872-7565
pubmed:author
pubmed:issnType
Electronic
pubmed:volume
95
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
10-22
pubmed:meshHeading
pubmed:year
2009
pubmed:articleTitle
Spermatogonium image recognition using Zernike moments.
pubmed:affiliation
College of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, China.
pubmed:publicationType
Journal Article, Research Support, Non-U.S. Gov't