Zobrazeno 1 - 10
of 30
pro vyhledávání: '"Mohamed Abou El-Ghar"'
Autor:
Ayman El-Baz, Ali Mahmoud, Fahmi Khalifa, Ahmed M. Soliman, Jasjit S. Suri, Mohammed Ghazal, Mohamed Shehata, Ahmed Shalaby, Maryam El-Baz, Amy C. Dwyer, Mohamed Abou El-Ghar, Shams Shaker
Publikováno v:
Machine Learning in Medicine ISBN: 9781315101323
Machine Learning in Medicine
Machine Learning in Medicine
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::d1883f0cc1eaa25de00818ffaa452e2a
https://doi.org/10.1201/9781315101323-14
https://doi.org/10.1201/9781315101323-14
Autor:
Mohamed Abou El-Ghar, Ayman El-Baz, Islam R. Abdelmaksoud, Ali Mahmoud, Moumen T. El-Melegy, Ahmed Shalaby, Norah Saleh Alghamdi, Mohammed Elmogy, Ahmed Aboelfetouh
Publikováno v:
Sensors, Vol 21, Iss 3664, p 3664 (2021)
Sensors (Basel, Switzerland)
Sensors
Volume 21
Issue 11
Sensors (Basel, Switzerland)
Sensors
Volume 21
Issue 11
Background and Objective: The use of computer-aided detection (CAD) systems can help radiologists make objective decisions and reduce the dependence on invasive techniques. In this study, a CAD system that detects and identifies prostate cancer from
Autor:
Moumen T. El-Melegy, Amy C. Dwyer, Mohamed Abou El-Ghar, Ayman El-Baz, Ali Mahmoud, Ashraf Bakr, Shams Shaker, Jasjit S. Suri, Ashraf Khalil, Mohamed Shehata, Mohammed Ghazal, Hisham Abdeltawab, Ahmed Shalaby
For the past several years the ability of diffusion-weighted magnetic resonance imaging (DW-MRI) to provide a noninvasive assessment of renal transplant function has been investigated. The goal of this chapter is to develop a computer-aided diagnosti
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::aa322efd9627a7fb2db9bc6d9fc62354
https://doi.org/10.1016/b978-0-12-819740-0.00005-x
https://doi.org/10.1016/b978-0-12-819740-0.00005-x
Publikováno v:
FUZZ-IEEE
Kidney segmentation from Dynamic Contrast Enhanced Magnetic Resonance Images (DCE-MRI) is a fundamental step for the early detection of transplanted kidney function. This paper presents an accurate and automatic DCE-MRI kidney segmentation method whi
Autor:
Ayman El-Baz, Robert S. Keynton, Mohammed Elmogy, Mohamed Abou El-Ghar, Mohammed Ghazal, Islam R. Abdelmaksoud, Ahmed Shalaby, Ahmed Aboulfotouh
Publikováno v:
IST
This paper proposes a computer-aided diagnosis (CAD) system for localizing prostate cancer from diffusion-weighted magnetic resonance imaging (DW-MRI). This system uses DW-MRI data sets that were acquired at four b-values: 100, 200, 300, and 400 smm
Autor:
Ali Mahmoud, Ayman El-Baz, Mohamed Abou El-Ghar, Ahmed Aboelfetouh, Mohammed Ghazal, Islam Reda, Ashraf Khalil, Ahmed Shalaby, Mohammed Elmogy
The goal of this chapter is to build a computer-aided diagnosis (CAD) system for diagnosing prostate cancer from diffusion-weighted magnetic resonance imaging (DWI). The first step in the proposed system segments the prostate using a level-set model.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::cb408830ff8aa916809c2f0fe246bf62
https://doi.org/10.1201/b22410-11
https://doi.org/10.1201/b22410-11
Autor:
Robert S. Keynton, Ahmed Shalaby, Ayman El-Baz, Ahmed Aboulfotouh, Mohamed Abou El-Ghar, Mohammed Ghazal, Mohammed Elmogy, Ashraf Khalil, Islam Reda, Moumen T. El-Melegy
Publikováno v:
ICIP
The purpose of this work is to develop a computer-aided diagnosis (CAD) system for detecting and localizing prostate cancer from diffusion-weighted magnetic resonance imaging (DWI) acquired at five distinct b-values. The first step in the proposed sy
Autor:
Mohammed Ghazal, Moumen T. El-Melegy, Fahmi Khalifa, Ayman El-Baz, K. Hammouda, Mohamed Abou El-Ghar, H. E. Darwish
Publikováno v:
Sensors
Volume 21
Issue 20
Sensors (Basel, Switzerland)
Sensors, Vol 21, Iss 6708, p 6708 (2021)
Volume 21
Issue 20
Sensors (Basel, Switzerland)
Sensors, Vol 21, Iss 6708, p 6708 (2021)
Prostate cancer is a significant cause of morbidity and mortality in the USA. In this paper, we develop a computer-aided diagnostic (CAD) system for automated grade groups (GG) classification using digitized prostate biopsy specimens (PBSs). Our CAD
Autor:
Mohamed Abou El-Ghar, Islam Reda, Mohammed Elmogy, Ehsan Hosseini-Asl, Ayman El-Baz, Naoufel Werghi, Ahmed Shalaby, Ahmed Abou Elfotouh, Fahmi Khalifa, Georgy Gimel'farb
Publikováno v:
Computers in Biology and Medicine. 81:148-158
Early detection of prostate cancer increases chances of patients' survival. Our automated non-invasive system for computer-aided diagnosis (CAD) of prostate cancer segments the prostate on diffusion-weighted magnetic resonance images (DW-MRI) acquire
Autor:
Mohammed Ghazal, Robert S. Keynton, Ayman El-Baz, Ahmed Shalaby, Adel Elmaghraby, Mohammed Elmogy, Islam Reda, Mohamed Abou El-Ghar, Ahmed Aboulfotouh
Publikováno v:
ISBI
A computer-aided diagnosis (CAD) system for early detection of prostate cancer from diffusion-weighted magnetic resonance imaging (DWI) is proposed in this paper. The proposed system starts by defining a region of interest that includes the prostate