Fully Automated Segmentation Of Fluid Regions In Exudative Age Related Macular Degeneration Subjects Kernel Graph Cut In Neutrosophic Domain
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Fully Automated Segmentation Of Fluid Regions In Exudative Age Related Macular Degeneration Subjects Kernel Graph Cut In Neutrosophic Domain
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Author : Abdolreza Rashno
language : en
Publisher: Infinite Study
Release Date :
Fully Automated Segmentation Of Fluid Regions In Exudative Age Related Macular Degeneration Subjects Kernel Graph Cut In Neutrosophic Domain written by Abdolreza Rashno and has been published by Infinite Study this book supported file pdf, txt, epub, kindle and other format this book has been release on with categories.
A fully-automated method based on graph shortest path, graph cut and neutrosophic (NS) sets is presented for fluid segmentation in OCT volumes for exudative age related macular degeneration (EAMD) subjects. The proposed method includes three main steps: 1) The inner limiting membrane (ILM) and the retinal pigment epithelium (RPE) layers are segmented using proposed methods based on graph shortest path in NS domain.
Deep Learning Approach For The Detection And Quantification Of Intraretinal Cystoid Fluid In Multivendor Optical Coherence Tomography
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Author : FREERK G. VENHUIZEN
language : en
Publisher: Infinite Study
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Deep Learning Approach For The Detection And Quantification Of Intraretinal Cystoid Fluid In Multivendor Optical Coherence Tomography written by FREERK G. VENHUIZEN and has been published by Infinite Study this book supported file pdf, txt, epub, kindle and other format this book has been release on with categories.
We developed a deep learning algorithm for the automatic segmentation and quantification of intraretinal cystoid fluid (IRC) in spectral domain optical coherence tomography (SD-OCT) volumes independent of the device used for acquisition.
Intraretinal Fluid Identification Via Enhanced Maps Using Optical Coherence Tomography Images
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Author : PLÁCIDO L. VIDAL
language : en
Publisher: Infinite Study
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Intraretinal Fluid Identification Via Enhanced Maps Using Optical Coherence Tomography Images written by PLÁCIDO L. VIDAL and has been published by Infinite Study this book supported file pdf, txt, epub, kindle and other format this book has been release on with Medical categories.
Nowadays, among the main causes of blindness in developed countries are age-related macular degeneration (AMD) and the diabetic macular edema (DME). Both diseases present, as a common symptom, the appearance of cystoid fluid regions inside the retinal layers. Optical coherence tomography (OCT) image modality was one of the main medical imaging techniques for the early diagnosis and monitoring of AMD and DME via this intraretinal fluid detection and characterization.
Diabetes And Retinopathy
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Author : Ayman S. El-Baz
language : en
Publisher: Elsevier
Release Date : 2020-05-12
Diabetes And Retinopathy written by Ayman S. El-Baz and has been published by Elsevier this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-05-12 with Medical categories.
Diabetes and Retinopathy brings together the multifaceted information about the research and clinical application from academic, clinical, bioengineering and bioinformatics perspectives. The editors bring together a stellar cast of authors to pull together this diverse and interesting field. Academic researchers, bioengineers, new investigators and students interested in diabetes and retinopathy need an authoritative reference to bring this multidisciplinary field together to reduce the amount of time spent on source-searching and more time on actual research and the clinical application. This reference depicts the current clinical understanding of DR as well as the many scientific advances in understanding this condition. - Provides valuable information for academic clinicians, researchers, bioengineers and industry on diabetes and retinopathy - Discusses the impact of diabetic retinopathy, a major cause of new-onset visual loss in all the industrialized nations - Covers statistical classification techniques and risk stratification
Intelligent Computing
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Author : Kohei Arai
language : en
Publisher: Springer
Release Date : 2019-06-22
Intelligent Computing written by Kohei Arai and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-06-22 with Computers categories.
This book presents the proceedings of the Computing Conference 2019, providing a comprehensive collection of chapters focusing on core areas of computing and their real-world applications. Computing is an extremely broad discipline, encompassing a range of specialized fields, each focusing on particular areas of technology and types of application, and the conference offered pioneering researchers, scientists, industrial engineers, and students from around the globe a platform to share new ideas and development experiences. Providing state-of-the-art intelligent methods and techniques for solving real- world problems, the book inspires further research and technological advances in this important area.
A Convoloutional Neural Network Model Based On Neutrosophy For Noisy Speech Recognition
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Author : Elyas Rashno
language : un
Publisher: Infinite Study
Release Date :
A Convoloutional Neural Network Model Based On Neutrosophy For Noisy Speech Recognition written by Elyas Rashno and has been published by Infinite Study this book supported file pdf, txt, epub, kindle and other format this book has been release on with Mathematics categories.
Convolutional neural networks are sensitive to unknown noisy condition in the test phase and so their performance degrades for the noisy data classification task including noisy speech recognition. In this research, a new convolutional neural network (CNN) model with data uncertainty handling; referred as NCNN (Neutrosophic Convolutional Neural Network); is proposed for classification task. Here, speech signals are used as input data and their noise is modeled as uncertainty.
Certainty Of Outlier And Boundary Points Processing In Data Mining
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Author : Elyas Rashno
language : un
Publisher: Infinite Study
Release Date :
Certainty Of Outlier And Boundary Points Processing In Data Mining written by Elyas Rashno and has been published by Infinite Study this book supported file pdf, txt, epub, kindle and other format this book has been release on with Mathematics categories.
Data certainty is one of the issues in the real-world applications which is caused by unwanted noise in data. Recently, more attentions have been paid to overcome this problem. We proposed a new method based on neutrosophic set (NS) theory to detect boundary and outlier points as challenging points in clustering methods.
Fully Automated Segmentation Of Fluid Cyst Regions In Optical Coherence Tomography Images With Diabetic Macular Edema Using Neutrosophic Sets And Graph Algorithms
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Author : Abdolreza Rashno
language : en
Publisher: Infinite Study
Release Date :
Fully Automated Segmentation Of Fluid Cyst Regions In Optical Coherence Tomography Images With Diabetic Macular Edema Using Neutrosophic Sets And Graph Algorithms written by Abdolreza Rashno and has been published by Infinite Study this book supported file pdf, txt, epub, kindle and other format this book has been release on with categories.
This paper presents a fully-automated algorithm to segment fluid-associated (fluid-filled) and cyst regions in optical coherence tomography (OCT) retina images of subjects with diabetic macular edema (DME).
Fluid Segmentation In Neutrosophic Domain
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Author : Elyas Rashno
language : en
Publisher: Infinite Study
Release Date :
Fluid Segmentation In Neutrosophic Domain written by Elyas Rashno and has been published by Infinite Study this book supported file pdf, txt, epub, kindle and other format this book has been release on with Mathematics categories.
Optical coherence tomography (OCT) as retina imaging technology is currently used by ophthalmologist as a non-invasive and non-contact method for diagnosis of agerelated degeneration (AMD) and diabetic macular edema (DME) diseases. Fluid regions in OCT images reveal the main signs of AMD and DME. In this paper, an efficient and fast clustering in neutrosophic (NS) domain referred as neutrosophic C-means is adapted for fluid segmentation. For this task, a NCM cost function in NS domain is adapted for fluid segmentation and then optimized by gradient descend methods which leads to binary segmentation of OCT Bscans to fluid and tissue regions. The proposed method is evaluated in OCT datasets of subjects with DME abnormalities. Results showed that the proposed method outperforms existing fluid segmentation methods by 6% in dice coefficient and sensitivity criteria.
Deep Learning Of Unified Region Edge And Contour Models For Automated Image Segmentation
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Author : Ali Hatamizadeh
language : en
Publisher:
Release Date : 2020
Deep Learning Of Unified Region Edge And Contour Models For Automated Image Segmentation written by Ali Hatamizadeh and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020 with categories.
Image segmentation is a fundamental and challenging problem in computer vision with applications spanning multiple areas, such as medical imaging, remote sensing, and autonomous vehicles. Recently, convolutional neural networks (CNNs) have gained traction in the design of automated segmentation pipelines. Although CNN-based models are adept at learning abstract features from raw image data, their performance is dependent on the availability and size of suitable training datasets. Additionally, these models are often unable to capture the details of object boundaries and generalize poorly to unseen classes. In this thesis, we devise novel methodologies that address these issues and establish robust representation learning frameworks for fully-automatic semantic segmentation in medical imaging and mainstream computer vision. In particular, our contributions include (1) state-of-the-art 2D and 3D image segmentation networks for computer vision and medical image analysis, (2) an end-to-end trainable image segmentation framework that unifies CNNs and active contour models with learnable parameters for fast and robust object delineation, (3) a novel approach for disentangling edge and texture processing in segmentation networks, and (4) a novel few-shot learning model in both supervised settings and semi-supervised settings where synergies between latent and image spaces are leveraged to learn to segment images given limited training data.