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Automatic heart localization for cardiac MRI perfusion imaging / Doaa Ali Ahmed Mousa ; Supervised Yasser Mostafa Kadah , Inas Ahmed Yassine , Nourhan Mohamed H. Zayed

By: Contributor(s): Material type: TextTextLanguage: English Publication details: Cairo : Doaa Ali Ahmed Mousa , 2016Description: 57 P. : charts , facsimiles ; 30cmOther title:
  • التحديد الأوتوماتيكى لموقع القلب فى صور الرنين المغناطيسى التباينى [Added title page title]
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Dissertation note: Thesis (M.Sc.) - Cairo University - Faculty of Engineering - Department of Systems and Biomedical Engineering Summary: We propose a software solution that can improve the analysis of contrast-enhanced magnetic resonance imaging (CMR perfusion) in simple and effective way. The proposed workflow consists of six main steps: Preprocessing step, Localization step, initial contour extraction, registration, segmentation, and evaluation and validation of results. This thesis gives a detailed implementation for each mentioned steps. The algorithm, implemented using MATLAB, was developed and tested using 3 datasets
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Item type Current library Home library Call number Copy number Status Date due Barcode
Thesis Thesis قاعة الرسائل الجامعية - الدور الاول المكتبة المركزبة الجديدة - جامعة القاهرة Cai01.13.03.M.Sc.2016.Do.A (Browse shelf(Opens below)) Not for loan 01010110069542000
CD - Rom CD - Rom مخـــزن الرســائل الجـــامعية - البدروم المكتبة المركزبة الجديدة - جامعة القاهرة Cai01.13.03.M.Sc.2016.Do.A (Browse shelf(Opens below)) 69542.CD Not for loan 01020110069542000

Thesis (M.Sc.) - Cairo University - Faculty of Engineering - Department of Systems and Biomedical Engineering

We propose a software solution that can improve the analysis of contrast-enhanced magnetic resonance imaging (CMR perfusion) in simple and effective way. The proposed workflow consists of six main steps: Preprocessing step, Localization step, initial contour extraction, registration, segmentation, and evaluation and validation of results. This thesis gives a detailed implementation for each mentioned steps. The algorithm, implemented using MATLAB, was developed and tested using 3 datasets

Issued also as CD

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