Common carotid artery condition recognition technology using waveform features extracted from ultrasound spectrum images

Authors:
Chiu-Mei Chen;Chiao-Min Chen;Hsien-Chu Wu;Chwei-Shyong Tsai
Affiliations:
School of Medicine, Chung Shan Medical University, 110, Sec. 1, Jianguo N. Rd., Taichung 402, Taiwan, ROC;Department of Management Information Systems, National Chung Hsing University, 250 Kuo Kuang Rd., Taichung 402, Taiwan, ROC;Department of Computer Science and Information Engineering, National Taichung University of Science and Technology, 129, Sec. 3, San Min Rd., Taichung 404, Taiwan, ROC;Department of Management Information Systems, National Chung Hsing University, 250 Kuo Kuang Rd., Taichung 402, Taiwan, ROC
Venue:
Journal of Systems and Software
Year:
2013

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Abstract

Medical image recognition algorithms have been widely applied to help with the diagnoses of various diseases, reducing human resource investment while enhancing diagnostic accuracy. This paper proposes a new scheme that specifies in the reading of ultrasound spectrum images of common carotid artery blood flow. The proposed scheme automatically extracts effective waveform features from the images for diagnostic purposes by using five criteria, which are ratio of waveform region, waveform region area target under horizontal baseline, waveform region area under horizontal baseline, highest point of waveform region, and lowest point of waveform region. Traditionally used by physicians to differentiate between normal blood flow patterns and five abnormal blood flow types, these five criteria are now employed by the new scheme to digitally diagnose vascular disorders at an accuracy rate as high as 0.97.