NDLI: Carotid ultrasound symptomatology using atherosclerotic plaque characterization: A class of Atheromatic systems

Content Provider	IEEE Xplore Digital Library
Author	Acharya, U.R. Sree, V.S. Molinari, F. Saba, L. Nicolaides, A. Shafique, S. Suri, J.S.
Copyright Year	2012
Description	Author affiliation: Global Biomed. Technol., Roseville, CA, USA (Suri, J.S.) \|\| Dept. of Radiol., Azienda Ospedaliero Univ. di Cagliari, Cagliari, Italy (Saba, L.) \|\| Dept. of Biol. Sci., Univ. of Cyprus, Nicosia, Cyprus (Nicolaides, A.) \|\| Dept. of Electron. & Comput. Eng., Ngee Ann Polytech., Singapore, Singapore (Acharya, U.R.) \|\| Dept. of Electron. & Telecommun., Politec. di Torino, Turin, Italy (Molinari, F.) \|\| Global Biomed. Technol. Inc., Roseville, CA, USA (Sree, V.S.) \|\| CorVas, West Lafayette, IN, USA (Shafique, S.)
Abstract	In this paper, we present a Computer Aided Diagnosis (CAD) based technique (Atheromatic system) for classification of carotid plaques in B-mode ultrasound images into symptomatic or asymptomatic classes. This system, called Atheromatic, has two steps: (i) feature extraction using a combination of Discrete Wavelet Transform (DWT) and averaging algorithms and (ii) classification using Support Vector Machine (SVM) classifier for automated decision making. The CAD system was built and tested using a database consisting of 150 asymptomatic and 196 symptomatic plaque regions of interests which were manually segmented. The ground truth of each plaque was determined based on the presence or absence of symptoms. Three-fold cross-validation protocol was adapted for developing and testing the classifiers. The SVM classifier with a polynomial kernel of order 2 recorded the highest classification accuracy of 83.7%. In the clinical scenario, such a technique, after much more validation, can be used as an adjunct tool to aid physicians by giving a second opinion on the nature of the plaque (symptomatic/asymptomatic) which would help in the more confident determination of the subsequent treatment regime for the patient.
Sponsorship	IEEE Eng. Medicine Biol. Soc.
Starting Page	3199
Ending Page	3202
File Size	428179
Page Count	4
File Format	PDF
ISBN	9781424441198
ISSN	1557170X
e-ISBN	9781457717871
DOI	10.1109/EMBC.2012.6346645
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-08-28
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Discrete wavelet transforms Support vector machines Ultrasonic imaging Feature extraction Accuracy Atherosclerosis classification Carotid ultrasound Grayscale Features Discrete Wavelet Transform Support Vector Machine (SVM)
Content Type	Text
Resource Type	Article
Subject	Signal Processing Biomedical Engineering Health Informatics Computer Vision and Pattern Recognition

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