Linked Life Data

22003701

Source:http://linkedlifedata.com/resource/pubmed/id/22003701

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
Pt 3
pubmed:dateCreated
2011-10-18
pubmed:abstractText
Ground glass nodules (GGNs) occur less frequent in computed tomography (CT) scans than solid nodules but have a much higher chance of being malignant. Accurate detection of these nodules is therefore highly important. A complete system for computer-aided detection of GGNs is presented consisting of initial segmentation steps, candidate detection, feature extraction and a two-stage classification process. A rich set of intensity, shape and context features is constructed to describe the appearance of GGN candidates. We apply a two-stage classification approach using a linear discriminant classifier and a GentleBoost classifier to efficiently classify candidate regions. The system is trained and independently tested on 140 scans that contained one or more GGNs from around 10,000 scans obtained in a lung cancer screening trial. The system shows a high sensitivity of 73% at only one false positive per scan.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:author
pubmed:volume
14
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
207-14
pubmed:meshHeading
pubmed:year
2011
pubmed:articleTitle
Computer-aided detection of ground glass nodules in thoracic CT images using shape, intensity and context features.
pubmed:affiliation
Fraunhofer MEVIS, Bremen, Germany.
pubmed:publicationType
Journal Article