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گواهی نمایه سازی مقاله Pseudo-increasing Frame Rates of Carotid Artery B-mode Images using Manifold Learning

عنوان مقاله: Pseudo-increasing Frame Rates of Carotid Artery B-mode Images using Manifold Learning
شناسه (COI) مقاله: ICBME20_055
منتشر شده در بیستمین کنفرانس مهندسی زیست پزشکی ایران در سال ۱۳۹۲
مشخصات نویسندگان مقاله:

Fereshteh Yousefi Rizi - Control and Intelligent Processing Center of Excellence, School of Electrical and Computer Engineering, College of Engineering, University of Tehran Tehran, Iran
Seyed Kamaledin Setarehdan - Control and Intelligent Processing Center of Excellence, School of Electrical and Computer Engineering, College of Engineering, University of Tehran Tehran, Iran
Hamid Behnam - School of Electrical Engineering, Iran University of Science and Technology, Tehran, Iran

خلاصه مقاله:
Tracking the rapid motions and vibrations of the carotid artery wall is an important issue in the noninvasive carotid ultasonography. In order to capture the fast movingstructures and vibrations, high frame rate B-mode sequences, without decreasing lateral resolution, are needed. In the presentpaper, manifold learning is used to find the relation between the frames during a cardiac cycle as well as multiple cardiac cycles.Locally linear embeddings method represents the nonlinearembedded information in the sequential B-mode images in a twodimensional manifold and each image is shown by a point onreconstructed manifold. The relationship between images in the new domain is obtained based on the periodicity of the cardiaccycle. Knowing the phase of the cardiac cycle for every frame helps the automatic detection of end-diastole and end-systole frames of carotid B-mode images which is helpful for noninvaisive elastography and carotid wall studies. The results show that our proposed method is capable of increasing frame rate effectively

کلمات کلیدی:
carotid B-mode images , frame rate , manifold learning , locally linear embeddings algorithm

صفحه اختصاصی مقاله و دریافت فایل کامل: https://www.civilica.com/Paper-ICBME20-ICBME20_055.html