Linked Life Data

19796916

Source:http://linkedlifedata.com/resource/pubmed/id/19796916

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
3
pubmed:dateCreated
2010-3-1
pubmed:abstractText
The neuroanatomical morphology of the optic nerve is an important description for understanding different aspects like topological distribution of nerves. Manual identification and morphometry has been usually considered as tedious, time consuming, and susceptible to error. A method that automates the identification and analysis of axons from electron micrographic images is presented. First, using region growing approach binarizes the image by combining the feature information together with spatial information, and obtains a coarse classification between myelin and non-myelin pixels. Next, identifies the axon candidates by region labeling and remove false axons on the basis of the identification ruler. Then the connected myelin sheaths are separated from each other using the maximum gradient magnitude of the outer annulus. Finally, analyses the morphological data of fibers. The developed method has been tested on a number of optic nerve images and results were presented. Regional distributions of axon caliber were unimodal. The thickness of the myelin sheath was highly correlated with the fiber diameter; hence, myelin sheath width was also distributed in a unimodal manner.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:month
Apr
pubmed:issn
1879-0771
pubmed:author
pubmed:copyrightInfo
Copyright (c) 2009 Elsevier Ltd. All rights reserved.
pubmed:issnType
Electronic
pubmed:volume
34
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
179-84
pubmed:meshHeading
pubmed:year
2010
pubmed:articleTitle
Automatic identification and morphometry of optic nerve fibers in electron microscopy images.
pubmed:affiliation
College of Information Engineering, Qingdao University, Qingdao, China.
pubmed:publicationType
Journal Article, Research Support, Non-U.S. Gov't