"A diffusion-weighted imaging based diagnostic system for early detection of prostate cancer"
written by Ahmad Firjani, Ahmed Elnakib, Fahmi Khalifa, Georgy Gimel’farb, Mohamed Abou El-Ghar, Adel Elmaghraby, Ayman El-Baz,
published by Journal of Biomedical Science and Engineering, Vol.6 No.3A, 2013
has been cited by the following article(s):
  • Google Scholar
  • CrossRef
[1] A new CNN-based system for early diagnosis of prostate cancer
2018
[2] Computer-aided diagnosis of clinically significant prostate cancer from MRI images using sparse autoencoder and random forest classifier
Biocybernetics and Biomedical Engineering, 2018
[3] A Novel ADCs-Based CNN Classification System for Precise Diagnosis of Prostate Cancer
2018
[4] A New Fast Framework for Early Detection of Prostate Cancer Without Prostate Segmentation
2018
[5] Computer-Aided Diagnosis of Prostate Cancer on Diffusion Weighted Imaging: A Technical Review
2018
[6] Diffusion-weighted magnetic resonance imaging in diagnosing graft dysfunction: a non-invasive alternative to renal biopsy.
2017
[7] A novel MRA-based framework for the detection of changes in cerebrovascular blood pressure.
2017
[8] Deformable model-based methods for image segmentation................... and Robert Keynton
Biomedical Image Segmentation, 2016
[9] A new NMF-autoencoder based CAD system for early diagnosis of prostate cancer
2016
[10] Application of an unsupervised multi-characteristic framework for intermediate-high risk prostate cancer localization using diffusion-weighted MRI
Magnetic Resonance Imaging, 2016
[11] Computational methods for the analysis of functional 4D-CT chest images.
2016
[12] A Comprehensive Non-invasive Framework for Diagnosing Prostate Cancer
Computers in Biology and Medicine, 2016
[13] A CAD system for early diagnosis of autism using different imaging modalities.
2016
[14] In vivo MRI based prostate cancer localization with random forests and auto-context model
Computerized Medical Imaging and Graphics, 2016
[15] Sparse feature learning for image analysis in segmentation, classification, and disease diagnosis.
2016
[16] A novel NMF-based CAD system for early diagnosis of prostate cancer by using 4-D diffusion-weighted magnetic resonance images (DW-MRI)
2016
[17] Image-Based Computer-Aided Diagnostic System for Early Diagnosis of Prostate Cancer
Medical Image Computing and Computer-Assisted Intervention – MICCAI 2016, 2016
[18] A non-invasive diagnostic system for early assessment of acute renal transplant rejection.
2016
[19] Computer-aided diagnostic tool for early detection of prostate cancer
2016
[20] Biomedical Image Segmentation: Advances & Trends
2016
[21] A novel framework for automatic segmentation of kidney from DW-MRI
2015
[22] Using morphological transforms to enhance the contrast of medical images
The Egyptian Journal of Radiology and Nuclear Medicine, 2015
[23] Computerized detection of cancer in multi-parametric prostate MRI
2015
[24] A level set-based framework for 3D kidney segmentation from diffusion MR images
Image Processing (ICIP), 2015 IEEE International Conference on, 2015
[25] Fast and robust hybrid framework for infant brain classification from structural MRI: a case study for early diagnosis of autism.
2014
[26] In-vitro and in-vivo diagnostic techniques for prostate cancer: A review
Journal of Biomedical Nanotechnology, 2014
[27] Geert Litjens is with Diagnostic Image Analysis Group, Radboud University Nijmegen Medical Centre, Geert Grootteplein-Zuid 10, 6525GA Nijmegen, The Netherlands.(email: g. litjens@ rad. umcn. nl)
2014
[28] A Novel NMF Guided Level-set for DWI Prostate Segmentation
J Comput Sci Syst Biol, 2014
[29] FAST AND ROBUST HYBRID FRAMEWORK FOR INFANT BRAIN CLASSIFICATION FROM STRUCTRUAL MRI: A CASE STUDY FOR EARLY DIAGNOSIS OF AUTISM
Doctoral dissertation, University of Louisville, 2014
[30] A NOVEL NMF-BASED DWI CAD FRAMEWORK FOR PROSTATE CANCER
Doctoral dissertation, University of Louisville, 2014
[31] ANALYSIS OF CONTRAST-ENHANCED MEDICAL IMAGES
Doctoral dissertation, University of Louisville, 2014
[32] A novel NMF-based DWI CAD framework for prostate cancer.
2014
[33] Analysis of contrast-enhanced medical images.
2014
[34] MRI-based diagnostic system for early detection of prostate cancer
Biomedical Sciences and Engineering Conference (BSEC), 2013. IEEE, 2013