Rasha Kamel
5 Papers
3 Citations
Rasha Kamel is an academic researcher. The author has contributed to research in topics: Computer science & Medicine. The author has an hindex of 2, co-authored 5 publications.
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Papers
Kidney segmentation from DCE-MRI converging level set methods, fuzzy clustering and Markov random field modeling
Moumen T. El-Melegy,Rasha Kamel,Mohamed Abou El-Ghar,Mohamed Shehata,Fahmi Khalifa,Ayman El-Baz +5 more
TL;DR: Wang et al. as discussed by the authors proposed an automated and accurate DCE-MRI kidney segmentation method integrating fuzzy c-means clustering and Markov random field modeling into a level set formulation.
Level-Set-Based Kidney Segmentation from DCE-MRI Using Fuzzy Clustering with Population-Based and Subject-Specific Shape Statistics
TL;DR: In this paper , a new and accurate DCE-MRI kidney segmentation method is proposed, where fuzzy c-means clustering is embedded into a level set method, with the fuzzy memberships being iteratively updated during the level set contour evolution.
Variational Approach for Joint Kidney Segmentation and Registration from DCE-MRI Using Fuzzy Clustering with Shape Priors
Moumen T. El-Melegy,Rasha Kamel,Mohamed Ibrahim Abou El-Ghar,Norah Saleh Alghamdi,Ayman El-Baz +4 more
TL;DR: In this article , a new variational formulation is proposed to jointly segment and register DCE-MRI kidney images based on fuzzy c-means clustering embedded within a level-set (LSet) method.
Discrimination between phyllodes tumor and fibro-adenoma: Does artificial intelligence-aided mammograms have an impact?
TL;DR: In this paper , the authors assess the ability of artificial intelligence-aided mammograms to aid the ultrasound in the discrimination between phyllodes tumors and fibro-adenomas, and the results show that the AI abnormality scoring of 49.5% upgraded the sensitivity to 89.6% and specificity to 94.8% in the ability to discriminate PT from FA masses.
Kidney Segmentation from Dynamic Contrast-Enhanced Magnetic Resonance Imaging Integrating Deep Convolutional Neural Networks and Level Set Methods
Moumen T. El-Melegy,Rasha Kamel,Mohamed Ibrahim Abou El-Ghar,Norah Saleh Alghamdi,Ayman El-Baz +4 more
TL;DR: Wang et al. as mentioned in this paper proposed a two-phase approach that integrates convolutional neural networks and level set methods to delimit kidneys in dynamic contrastenhanced magnetic resonance imaging (DCE-MRI) scans.