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

Mougiakakou, Stavroula G R; Golemati, Spyretta; Gousias, Ioannis; Nicolaides, Andrew N; Nikita, Konstantina S (2007). Computer-aided diagnosis of carotid atherosclerosis based on ultrasound image statistics, laws' texture and neural networks. Ultrasound in medicine & biology, 33(1), pp. 26-36. Tarrytown, NY: Elsevier 10.1016/j.ultrasmedbio.2006.07.032

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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.

Item Type:	Journal Article (Original Article)
Division/Institute:	04 Faculty of Medicine > Department of Gynaecology, Paediatrics and Endocrinology (DFKE) > Clinic of Endocrinology, Diabetology and Clinical Nutrition
UniBE Contributor:	Mougiakakou, Stavroula
ISSN:	0301-5629
Publisher:	Elsevier
Language:	English
Submitter:	Factscience Import
Date Deposited:	04 Oct 2013 15:23
Last Modified:	05 Dec 2022 14:25
Publisher DOI:	10.1016/j.ultrasmedbio.2006.07.032
PubMed ID:	17189044
Web of Science ID:	000243243700004
URI:	https://boris.unibe.ch/id/eprint/37261 (FactScience: 207285)