Computer-Aided Diagnosis of Carotid Atherosclerosis using Laws' Texture Features and a Hybrid Trained Neural Network

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

dc.contributor.author	Mougiakakou, SGr	en
dc.contributor.author	Golemati, S	en
dc.contributor.author	Gousias, I	en
dc.contributor.author	Nikita, KS	en
dc.contributor.author	Nicolaides, AN	en
dc.date.accessioned	2014-03-01T02:42:13Z
dc.date.available	2014-03-01T02:42:13Z
dc.date.issued	2003	en
dc.identifier.issn	05891019	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/30871
dc.subject	Carotid atherosclerosis	en
dc.subject	Classification	en
dc.subject	Genetic algorithms	en
dc.subject	Laws' texture	en
dc.subject	Neural networks	en
dc.subject	Ultrasound	en
dc.subject.other	Backpropagation	en
dc.subject.other	Computer aided diagnosis	en
dc.subject.other	Feature extraction	en
dc.subject.other	Genetic algorithms	en
dc.subject.other	Medical imaging	en
dc.subject.other	Neural networks	en
dc.subject.other	Statistics	en
dc.subject.other	Textures	en
dc.subject.other	Ultrasonics	en
dc.subject.other	Momentum	en
dc.subject.other	Pixels	en
dc.subject.other	Disease control	en
dc.title	Computer-Aided Diagnosis of Carotid Atherosclerosis using Laws' Texture Features and a Hybrid Trained Neural Network	en
heal.type	conferenceItem	en
heal.identifier.primary	10.1109/IEMBS.2003.1279484	en
heal.identifier.secondary	http://dx.doi.org/10.1109/IEMBS.2003.1279484	en
heal.publicationDate	2003	en
heal.abstract	Objective diagnosis of carotid atherosclerosis and classification into symptomatic or asymptomatic is crucial in planning optimal treatment of atheromatous plaque. The Computer-Aided Diagnostic (CAD) system described in this paper can analyze B-mode ultrasound images of the carotid artery and classify them into Symptomatic (S) or Asymptomatic (A). Images from 54 S and 54 A plaques were fed to the CAD system, which consists of three modules: the feature extraction module, where texture features are estimated based on Laws' texture energy, the dimensionality reduction module, where the number of features is reduced using ANOVA statistics, and the classifier module with a Neural Network (NN) trained via a novel hybrid method in order to recognize the type of atheromatous plaques. The hybrid training method uses Genetic Algorithms (GA's) to locate a starting point close to the optimal solution, and then the back-propagation (BP) algorithm with adaptive learning rate and momentum to refine the NN configuration with local search. The hybrid method is able to select the most robust features, to adjust automatically the NN architecture, and to optimize the classification performance. The proposed CAD system has achieved a total classification performance of 99%.	en
heal.journalName	Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings	en
dc.identifier.doi	10.1109/IEMBS.2003.1279484	en
dc.identifier.volume	2	en
dc.identifier.spage	1248	en
dc.identifier.epage	1251	en