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Proceedings Paper

BP network for atorvastatin effect evaluation from ultrasound images features classification
Author(s): Mengjie Fang; Xin Yang; Yang Liu; Hongwei Xu; Huageng Liang; Yujie Wang; Mingyue Ding
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Paper Abstract

Atherosclerotic lesions at the carotid artery are a major cause of emboli or atheromatous debris, resulting in approximately 88% of ischemic strokes in the USA in 2006. Stroke is becoming the most common cause of death worldwide, although patient management and prevention strategies have reduced stroke rate considerably over the past decades. Many research studies have been carried out on how to quantitatively evaluate local arterial effects for potential carotid disease treatments. As an inexpensive, convenient and fast means of detection, ultrasonic medical testing has been widespread in the world, so it is very practical to use ultrasound technology in the prevention and treatment of carotid atherosclerosis. This paper is dedicated to this field. Currently, many ultrasound image characteristics on carotid plaque have been proposed. After screening a large number of features (including 26 morphological and 85 texture features), we have got six shape characteristics and six texture characteristics in the combination. In order to test the validity and accuracy of these combined features, we have established a Back-Propagation (BP) neural network to classify atherosclerosis plaques between atorvastatin group and placebo group. The leave-one-case-out protocol was utilized on a database of 768 carotid ultrasound images of 12 patients (5 subjects of placebo group and 7 subjects of atorvastatin group) for the evaluation. The classification results showed that the combined features and classification have good recognition ability, with the overall accuracy 83.93%, sensitivity 82.14%, specificity 85.20%, positive predictive value 79.86%, negative predictive value 86.98%, Matthew’s correlation coefficient 67.08%, and Youden’s index 67.34%. And the receiver operating characteristic (ROC) curve in our test also performed well.

Paper Details

Date Published: 27 October 2013
PDF: 8 pages
Proc. SPIE 8919, MIPPR 2013: Pattern Recognition and Computer Vision, 891905 (27 October 2013); doi: 10.1117/12.2030704
Show Author Affiliations
Mengjie Fang, Huazhong Univ. of Science and Technology (China)
Xin Yang, Huazhong Univ. of Science and Technology (China)
Yang Liu, Huazhong Univ. of Science and Technology (China)
Hongwei Xu, Huazhong Univ. of Science and Technology (China)
Huageng Liang, Huazhong Univ. of Science and Technology (China)
Yujie Wang, Huazhong Univ. of Science and Technology (China)
Mingyue Ding, Huazhong Univ. of Science and Technology (China)

Published in SPIE Proceedings Vol. 8919:
MIPPR 2013: Pattern Recognition and Computer Vision
Zhiguo Cao, Editor(s)

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