A diffusion-weighted imaging based diagnostic system for early detection of prostate cancer

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Journal of Biomedical Science and Engineering

ISSN Print: 1937-6871
ISSN Online: 1937-688X
www.scirp.org/journal/jbise
E-mail: jbise@scirp.org

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"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):

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[3]	The Classification Power of Classical and Intra-voxel Incoherent Motion (IVIM) Fitting Models of Diffusion-weighted Magnetic Resonance Images: An Experimental …
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[4]	An Enhanced Deep Learning Technique for Prostate Cancer Identification Based on MRI Scans
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[5]	Role of machine learning in early diagnosis of kidney diseases.
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[6]	To Determine the Diagnostic Accuracy of Diffusion-Weighted Imaging in the Diagnosis of Prostate Carcinoma Taking Histopathology As the Gold Standard
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[7]	Supervised classifiers of prostate cancer
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[8]	Big Data in Prostate Cancer
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[9]	Supervised classifiers of prostate cancer: A geometric study on magnetic resonance images T2 weighted (T2W), by diffusion (DWI-ADC)
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[10]	MRI Imaging of Seminal Vesicle Invasion (SVI) in Prostate Adenocarcinoma
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[11]	On Computer-Aided Diagnosis of Prostate Cancer from MRI using Machine Intelligence Techniques
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[12]	18 Diagnosing Prostate Cancer Based on Deep Learning with Constraint Autoencoder
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[13]	19 MRI Imaging of Seminal Vesicle Invasion (SVI) in Prostate Adenocarcinoma
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[14]	Diffusion-weighted MRI based System for the Early Detection of Prostate Cancer
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[15]	A new CNN-based system for early diagnosis of prostate cancer
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[16]	Computer-aided diagnosis of clinically significant prostate cancer from MRI images using sparse autoencoder and random forest classifier
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[17]	A Novel ADCs-Based CNN Classification System for Precise Diagnosis of Prostate Cancer
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[18]	A New Fast Framework for Early Detection of Prostate Cancer Without Prostate Segmentation
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[19]	Computer-Aided Diagnosis of Prostate Cancer on Diffusion Weighted Imaging: A Technical Review
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[20]	A Computer-Aided Diagnostic System for the Early Detection of Prostate Cancer Using Diffusion-Weighted Magnetic Resonance Imaging
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[21]	13 Prostate Segmentation from
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[22]	Prostate Segmentation from DW-MRI Using Level-Set Guided by Nonnegative Matrix Factorization
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[23]	Diagnosing Prostate Cancer Based on Deep Learning with a Stacked Nonnegativity Constraint Autoencoder
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[24]	A DCE-MRI-Based Noninvasive CAD System for Prostate Cancer Diagnosis
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[25]	Prostate Cancer Imaging: An Engineering and Clinical Perspective
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[26]	Early diagnosis and staging of prostate cancer using magnetic resonance imaging: State of the art and perspectives
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[30]	A new NMF-autoencoder based CAD system for early diagnosis of prostate cancer
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[31]	Application of an unsupervised multi-characteristic framework for intermediate-high risk prostate cancer localization using diffusion-weighted MRI
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[32]	Computational methods for the analysis of functional 4D-CT chest images.
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[33]	A Comprehensive Non-invasive Framework for Diagnosing Prostate Cancer
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[35]	In vivo MRI based prostate cancer localization with random forests and auto-context model
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[37]	A novel NMF-based CAD system for early diagnosis of prostate cancer by using 4-D diffusion-weighted magnetic resonance images (DW-MRI)
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[38]	Image-Based Computer-Aided Diagnostic System for Early Diagnosis of Prostate Cancer
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[40]	Computer-aided diagnostic tool for early detection of prostate cancer
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[41]	Biomedical Image Segmentation: Advances & Trends
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[42]	Prostate segmentation using deformable model-based methods: A review
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[43]	An appearance-guided deformable model for 4D kidney segmentation using diffusion MRI
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[44]	A novel NMF-based CAD system for early diagnosis of prostate cancer by using 4D diffusion-weighted magnetic resonance images (DW-MRI)
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[45]	A level set-based framework for 3D kidney segmentation from diffusion MR images
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[46]	A novel framework for automatic segmentation of kidney from DW-MRI
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[48]	Computerized detection of cancer in multi-parametric prostate MRI
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[49]	Fast and robust hybrid framework for infant brain classification from structural MRI: a case study for early diagnosis of autism.
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[50]	In-vitro and in-vivo diagnostic techniques for prostate cancer: A review
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[51]	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)
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[58]	ThinkIR: The University of Louisville's Institutional Repositor y
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[59]	MRI-based diagnostic system for early detection of prostate cancer
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[1]	State of the Art in Neural Networks and Their Applications 2023 DOI:10.1016/B978-0-12-819872-8.00011-2

[2]	Non-Invasive Estimation of Gleason Score by Semantic Segmentation and Regression Tasks Using a Three-Dimensional Convolutional Neural Network Applied Sciences, 2023 DOI:10.3390/app13148028

[3]	The Classification Power of Classical and Intra-voxel Incoherent Motion (IVIM) Fitting Models of Diffusion-weighted Magnetic Resonance Images: An Experimental Study Journal of Digital Imaging, 2022 DOI:10.1007/s10278-022-00604-z

[4]	To Determine the Diagnostic Accuracy of Diffusion-Weighted Imaging in the Diagnosis of Prostate Carcinoma Taking Histopathology As the Gold Standard Cureus, 2021 DOI:10.7759/cureus.19508

[5]	On Computer-Aided Diagnosis of Prostate Cancer from MRI using Machine Intelligence Techniques 2019 IEEE International Conference on Electrical, Computer and Communication Technologies (ICECCT), 2019 DOI:10.1109/ICECCT.2019.8869533

[6]	Computer-aided diagnosis of clinically significant prostate cancer from MRI images using sparse autoencoder and random forest classifier Biocybernetics and Biomedical Engineering, 2018 DOI:10.1016/j.bbe.2018.06.009

[7]	A new CNN-based system for early diagnosis of prostate cancer 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018), 2018 DOI:10.1109/ISBI.2018.8363556

[8]	A Novel ADCs-Based CNN Classification System for Precise Diagnosis of Prostate Cancer 2018 24th International Conference on Pattern Recognition (ICPR), 2018 DOI:10.1109/ICPR.2018.8546029

[9]	A New Fast Framework for Early Detection of Prostate Cancer Without Prostate Segmentation 2018 IEEE International Conference on Imaging Systems and Techniques (IST), 2018 DOI:10.1109/IST.2018.8577162

[10]	Computer-Aided Diagnosis of Prostate Cancer on Diffusion Weighted Imaging: A Technical Review 2018 IEEE International Conference on Imaging Systems and Techniques (IST), 2018 DOI:10.1109/IST.2018.8577082

[11]	A comprehensive non-invasive framework for diagnosing prostate cancer Computers in Biology and Medicine, 2017 DOI:10.1016/j.compbiomed.2016.12.010

[12]	Medical Image Computing and Computer-Assisted Intervention – MICCAI 2016 Lecture Notes in Computer Science, 2016 DOI:10.1007/978-3-319-46720-7_71

[13]	In vivo MRI based prostate cancer localization with random forests and auto-context model Computerized Medical Imaging and Graphics, 2016 DOI:10.1016/j.compmedimag.2016.02.001

[14]	A new NMF-autoencoder based CAD system for early diagnosis of prostate cancer 2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI), 2016 DOI:10.1109/ISBI.2016.7493490

[15]	Application of an unsupervised multi-characteristic framework for intermediate-high risk prostate cancer localization using diffusion-weighted MRI Magnetic Resonance Imaging, 2016 DOI:10.1016/j.mri.2016.06.004

[16]	Biomedical Image Segmentation 2016 DOI:10.4324/9781315372273-2

[17]	Biomedical Image Segmentation 2016 DOI:10.4324/9781315372273-15

[18]	Biomedical Image Segmentation 2016 DOI:10.4324/9781315372273-14

[19]	Biomedical Image Segmentation 2016 DOI:10.4324/9781315372273-13

[20]	Computer-aided diagnostic tool for early detection of prostate cancer 2016 IEEE International Conference on Image Processing (ICIP), 2016 DOI:10.1109/ICIP.2016.7532843

[21]	A level set-based framework for 3D kidney segmentation from diffusion MR images 2015 IEEE International Conference on Image Processing (ICIP), 2015 DOI:10.1109/ICIP.2015.7351646

[22]	Using morphological transforms to enhance the contrast of medical images The Egyptian Journal of Radiology and Nuclear Medicine, 2015 DOI:10.1016/j.ejrnm.2015.01.004

[23]	A novel framework for automatic segmentation of kidney from DW-MRI 2015 IEEE 12th International Symposium on Biomedical Imaging (ISBI), 2015 DOI:10.1109/ISBI.2015.7164028

[24]	Computer-Aided Detection of Prostate Cancer in MRI IEEE Transactions on Medical Imaging, 2014 DOI:10.1109/TMI.2014.2303821

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