BP network for atorvastatin effect evaluation from ultrasound images features classification

Mengjie Fang; Xin Yang; Yang Liu; Hongwei Xu; Huageng Liang; Yujie Wang; Mingyue Ding

doi:10.1117/12.2030704

27 October 2013 BP network for atorvastatin effect evaluation from ultrasound images features classification

Mengjie Fang, Xin Yang, Yang Liu, Hongwei Xu, Huageng Liang, Yujie Wang, Mingyue Ding

Proceedings Volume 8919, MIPPR 2013: Pattern Recognition and Computer Vision; 891905 (2013) https://doi.org/10.1117/12.2030704
Event: Eighth International Symposium on Multispectral Image Processing and Pattern Recognition, 2013, Wuhan, China

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.

Citation Download Citation

Mengjie Fang, Xin Yang, Yang Liu, Hongwei Xu, Huageng Liang, Yujie Wang, and Mingyue Ding "BP network for atorvastatin effect evaluation from ultrasound images features classification", Proc. SPIE 8919, MIPPR 2013: Pattern Recognition and Computer Vision, 891905 (27 October 2013); https://doi.org/10.1117/12.2030704

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KEYWORDS

Arteries

Ultrasonography

Image segmentation

3D image processing

Image classification

Neural networks

Simulation of CCA and DLA aggregates

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