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Wavelet-based fast computer-aided characterization of liver steatosis using conventional B-mode ultrasound images / Manar Nasser Amin Mahmoud ; Supervised Ahmed M. Ehab Mahmoud , Muhammad Ali Rushdi

By: Contributor(s): Material type: TextTextLanguage: English Publication details: Cairo : Manar Nasser Amin Mahmoud , 2017Description: 68 P. ; 30cmOther title:
  • تقنية سريعة لتوصيف الكبد الدهنى بمساعدة الحاسب و استخدام المويجات و الصور التقليدية للموجات فوق الصوتية [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: Hepatic steatosis occurs when lipids accumulate in the liver and can eventually liver failure requiring a liver transplant. This work develop a computationally-efficient technique to classify fatty liver using B-mode us images. The technique relies on extracting features from the Wavelet domain using the approximation part of us images. Features include the first-order gray-level parameters, co-occurrence matrices, and local binary patterns. The technique was tested using mouse livers and image of human livers. This technique shall improve the implementation of manufacturer independent real time techniques for fatty liver classification
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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.2017.Ma.W (Browse shelf(Opens below)) Not for loan 01010110074443000
CD - Rom CD - Rom مخـــزن الرســائل الجـــامعية - البدروم المكتبة المركزبة الجديدة - جامعة القاهرة Cai01.13.03.M.Sc.2017.Ma.W (Browse shelf(Opens below)) 74443.CD Not for loan 01020110074443000

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

Hepatic steatosis occurs when lipids accumulate in the liver and can eventually liver failure requiring a liver transplant. This work develop a computationally-efficient technique to classify fatty liver using B-mode us images. The technique relies on extracting features from the Wavelet domain using the approximation part of us images. Features include the first-order gray-level parameters, co-occurrence matrices, and local binary patterns. The technique was tested using mouse livers and image of human livers. This technique shall improve the implementation of manufacturer independent real time techniques for fatty liver classification

Issued also as CD

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