Computer-aided diagnosis of carotid atherosclerosis based on ultrasound image statistics, laws' texture and neural networks

Mougiakakou, SGr; Golemati, S; Gousias, I; Nicolaides, AN; Nikita, KS

dc.contributor.author	Mougiakakou, SGr	en
dc.contributor.author	Golemati, S	en
dc.contributor.author	Gousias, I	en
dc.contributor.author	Nicolaides, AN	en
dc.contributor.author	Nikita, KS	en
dc.date.accessioned	2014-03-01T01:26:02Z
dc.date.available	2014-03-01T01:26:02Z
dc.date.issued	2007	en
dc.identifier.issn	0301-5629	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/17896
dc.subject	Carotid atherosclerosis	en
dc.subject	Classification	en
dc.subject	Computer-aided diagnosis	en
dc.subject	Genetic algorithms	en
dc.subject	Laws' texture energy	en
dc.subject	Neural networks	en
dc.subject	ROC	en
dc.subject	Ultrasound	en
dc.subject.classification	Acoustics	en
dc.subject.classification	Radiology, Nuclear Medicine & Medical Imaging	en
dc.subject.other	Biological organs	en
dc.subject.other	Classification (of information)	en
dc.subject.other	Genetic algorithms	en
dc.subject.other	Medical imaging	en
dc.subject.other	Neural networks	en
dc.subject.other	Statistical methods	en
dc.subject.other	Ultrasonics	en
dc.subject.other	Analysis of variance (ANOVA),	en
dc.subject.other	Carotid atherosclerosis	en
dc.subject.other	Laws' texture energy	en
dc.subject.other	Ultrasound image statistics	en
dc.subject.other	Computer aided design	en
dc.subject.other	analysis of variance	en
dc.subject.other	article	en
dc.subject.other	atherosclerosis	en
dc.subject.other	atherosclerotic plaque	en
dc.subject.other	carotid artery	en
dc.subject.other	computer assisted diagnosis	en
dc.subject.other	human	en
dc.subject.other	image analysis	en
dc.subject.other	major clinical study	en
dc.subject.other	nerve cell network	en
dc.subject.other	priority journal	en
dc.subject.other	quantitative analysis	en
dc.subject.other	roc curve	en
dc.subject.other	statistics	en
dc.subject.other	stroke	en
dc.subject.other	symptomatology	en
dc.subject.other	training	en
dc.subject.other	ultrasound	en
dc.subject.other	Algorithms	en
dc.subject.other	Carotid Arteries	en
dc.subject.other	Carotid Artery Diseases	en
dc.subject.other	Humans	en
dc.subject.other	Image Interpretation, Computer-Assisted	en
dc.subject.other	Neural Networks (Computer)	en
dc.title	Computer-aided diagnosis of carotid atherosclerosis based on ultrasound image statistics, laws' texture and neural networks	en
heal.type	journalArticle	en
heal.identifier.primary	10.1016/j.ultrasmedbio.2006.07.032	en
heal.identifier.secondary	http://dx.doi.org/10.1016/j.ultrasmedbio.2006.07.032	en
heal.language	English	en
heal.publicationDate	2007	en
heal.abstract	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. (E-mail: knikita@cc.ece.ntua.gr) (c) 2006 World Federation for Ultrasound in Medicine & Biology.	en
heal.publisher	ELSEVIER SCIENCE INC	en
heal.journalName	Ultrasound in Medicine and Biology	en
dc.identifier.doi	10.1016/j.ultrasmedbio.2006.07.032	en
dc.identifier.isi	ISI:000243243700004	en
dc.identifier.volume	33	en
dc.identifier.issue	1	en
dc.identifier.spage	26	en
dc.identifier.epage	36	en