Linked Life Data

18754994

Source:http://linkedlifedata.com/resource/pubmed/id/18754994

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
3
pubmed:dateCreated
2008-8-29
pubmed:abstractText
Automated segmentation of time-lapse images is a method to facilitate the understanding of the intricate biological progression, e.g. cancer cell migration. To address this problem, we introduce a shape representation enhancement over popular snake models in the context of confident scale-space such that a higher level of interpretation can hopefully be achieved. Our proposed system consists of a hierarchical analytic framework including feedback loops, self-adaptive and demand-adaptive adjustment, incorporating a steerable boundary detail term constraint based on multiscale B-spline interpolation. To minimize the noise interference inherited from microscopy acquisition, the coarse boundary derived from the initial segmentation with refined watershed line is coupled with microscopy compensation using the mean shift filtering. A progressive approximation is applied to achieve represented as a balance between a relief function of watershed algorithm and local minima concerning multiscale optimality, convergence and robust constraints. Experimental results show that the proposed method overcomes problems with spurious branches, arbitrary gaps, low contrast boundaries and low signal-to-noise ratio. The proposed system has the potential to serve as an automated data processing tool for cell migration applications.
pubmed:grant
pubmed:commentsCorrections
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:month
Sep
pubmed:issn
1365-2818
pubmed:author
pubmed:issnType
Electronic
pubmed:volume
231
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
395-407
pubmed:dateRevised
2011-6-13
pubmed:meshHeading
pubmed:year
2008
pubmed:articleTitle
A confident scale-space shape representation framework for cell migration detection.
pubmed:affiliation
Department of Electronic Engineering, Shanghai Jiao Tong University, Shanghai, PR China.
pubmed:publicationType
Journal Article, Research Support, Non-U.S. Gov't, Research Support, N.I.H., Extramural