17189044

Source:http://linkedlifedata.com/resource/pubmed/id/17189044

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Predicate	Object
rdf:type	pubmed:Citation
lifeskim:mentions	umls-concept:C0011905, umls-concept:C0023150, umls-concept:C0038215, umls-concept:C0041618, umls-concept:C0220866, umls-concept:C0242406, umls-concept:C0449582, umls-concept:C0577631, umls-concept:C1527178, umls-concept:C1704922, umls-concept:C1705938
pubmed:issue	1
pubmed:dateCreated	2006-12-25
pubmed:abstractText	Quantitative characterisation of carotid atherosclerosis and classification into symptomatic or asymptomatic is crucial in planning optimal treatment of atheromatous plaque. The computer-aided diagnosis (CAD) system described in this paper can analyse ultrasound (US) images of carotid artery and classify them into symptomatic or asymptomatic based on their echogenicity characteristics. The CAD system consists of three modules: a) the feature extraction module, where first-order statistical (FOS) features and Laws' texture energy can be estimated, b) the dimensionality reduction module, where the number of features can be reduced using analysis of variance (ANOVA), and c) the classifier module consisting of a neural network (NN) trained by a novel hybrid method based on genetic algorithms (GAs) along with the back propagation algorithm. The hybrid method is able to select the most robust features, to adjust automatically the NN architecture and to optimise the classification performance. The performance is measured by the accuracy, sensitivity, specificity and the area under the receiver-operating characteristic (ROC) curve. The CAD design and development is based on images from 54 symptomatic and 54 asymptomatic plaques. This study demonstrates the ability of a CAD system based on US image analysis and a hybrid trained NN to identify atheromatous plaques at high risk of stroke.
pubmed:language	eng
pubmed:journal	http://linkedlifedata.com/resource/pubmed/journal/0410553
pubmed:citationSubset	IM
pubmed:status	MEDLINE
pubmed:month	Jan
pubmed:issn	0301-5629
pubmed:author	pubmed-author:GolematiSpyrettaS, pubmed-author:GousiasIoannisI, pubmed-author:MougiakakouStavroula G RSG, pubmed-author:NicolaidesAndrew NAN, pubmed-author:NikitaKonstantina SKS
pubmed:issnType	Print
pubmed:volume	33
pubmed:owner	NLM
pubmed:authorsComplete	Y
pubmed:pagination	26-36
pubmed:meshHeading	pubmed-meshheading:17189044-Algorithms, pubmed-meshheading:17189044-Carotid Arteries, pubmed-meshheading:17189044-Carotid Artery Diseases, pubmed-meshheading:17189044-Humans, pubmed-meshheading:17189044-Image Interpretation, Computer-Assisted, pubmed-meshheading:17189044-Neural Networks (Computer)
pubmed:year	2007
pubmed:articleTitle	Computer-aided diagnosis of carotid atherosclerosis based on ultrasound image statistics, laws' texture and neural networks.
pubmed:affiliation	Biomedical Simulations and Imaging Laboratory, Faculty of Electrical and Computer Engineering, National Technical University of Athens, Athens, Greece.
pubmed:publicationType	Journal Article, Research Support, Non-U.S. Gov't