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Author Cheng, Da-Chuan ♦ Schmidt-Trucksäss, Arno ♦ Liu, Chung-Hsiang ♦ Liu, Shing-Hong
Source World Health Organization (WHO)-Global Index Medicus
Content type Text
Publisher Multidisciplinary Digital Publishing Institute
File Format HTM / HTML
Language English
Difficulty Level Medium
Subject Domain (in DDC) Computer science, information & general works ♦ Library & information sciences ♦ Natural sciences & mathematics ♦ Life sciences; biology ♦ Natural history of organisms ♦ Technology ♦ Medicine & health ♦ Human anatomy, cytology, histology ♦ Diseases ♦ Manufacture for specific uses ♦ Precision instruments & other devices
Subject Domain (in MeSH) Cardiovascular System ♦ Tissues ♦ Anatomy ♦ Eukaryota ♦ Organisms ♦ Cardiovascular Diseases ♦ Diseases ♦ Diagnosis ♦ Analytical, Diagnostic and Therapeutic Techniques and Equipment ♦ Information Science ♦ Information Science
Subject Keyword Discipline Biotechnology ♦ Carotid Artery, Common ♦ Anatomy & Histology ♦ Atherosclerosis ♦ Diagnosis ♦ Pathology ♦ Connective Tissue ♦ Humans ♦ Image Interpretation, Computer-assisted ♦ Methods ♦ Pattern Recognition, Automated ♦ Signal Processing, Computer-assisted ♦ Instrumentation ♦ Tunica Intima ♦ Tunica Media ♦ Ultrasonography ♦ Evaluation Studies ♦ Journal Article ♦ Research Support, Non-u.s. Gov't
Abstract In this paper we propose a novel scheme able to automatically detect the intima and adventitia of both near and far walls of the common carotid artery in dynamic B-mode RF (radiofrequency) image sequences, with and without plaques. Via this automated system the lumen diameter changes along the heart cycle can be detected. Three image sequences have been tested and all results are compared to manual tracings made by two professional experts. The average errors for near and far wall detection are 0.058 mm and 0.067 mm, respectively. This system is able to analyze arterial plaques dynamically which is impossible to do manually due to the tremendous human workload involved.
Description Country affiliation: Taiwan
Author Affiliation: Cheng DC ( Department of Biomedical Imaging and Radiological Science, China Medical University, and Department of Neurology, China Medical University Hospital, Taichung, Taiwan.
Educational Role Student ♦ Teacher
Age Range above 22 year
Educational Use Reading ♦ Research ♦ Self Learning
Interactivity Type Expositive
Education Level UG and PG
Learning Resource Type Article
Publisher Date 2010-01-01
Publisher Place Switzerland
e-ISSN 14248220
Journal Sensors
Volume Number 10
Issue Number 12

Source: WHO-Global Index Medicus