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Automated Image Detection of Retinal Pathology

to the clinic. So what is the best way to diagnose and treat retinopathy? Automated Image Detection of Retinal Pathology discusses the;

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Retinal Optical Coherence Tomography Image Analysis

image preprocessing, as well as the automatic detection and quantitative analysis of retinal anatomy and pathology, it includes a wealth;

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Digital Image Processing for Ophthalmology

anatomical structures and pathological features in the retina. Different types of retinal pathology may be detected and analyzed via the application;

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Computational Retinal Image Analysis

Computational Retinal Image Analysis: Tools, Applications and Perspectives gives an overview of contemporary retinal image analysis (RIA;

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Hybrid Machine Intelligence for Medical Image Analysis

include automated region of interest detection of magnetic resonance images based on center of gravity; brain tumor detection through low-level;

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Image Analysis and Modeling in Ophthalmology

, and highlights specific imaging and modeling techniques. The book covers the detection of diabetes retinopathy, glaucoma, anterior segment;

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Computer Vision and Recognition Systems

improvement, Parkinson's disease diagnosis, hypertensive retinopathy detection through retinal fundus, big image data processing, N-grams for image;

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Image Analysis and Recognition

; shape representation; 3D imaging; applications; biometrics and face recognition; human activity recognition; biomedical image analysis; retinal;

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Image Analysis and Recognition

; shape representation; 3D imaging; applications; biometrics and face recognition; human activity recognition; biomedical image analysis; retinal;

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Image Analysis and Recognition

recognition; image processing and analysis: representations and models, compression, enhancement , feature detection and segmentation; 3D image;

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Diabetes and Fundus OCT

the retinal vasculature in diabetics as imaged by optical coherence tomography angiography and automated detection of retinal disease

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State of the Art in Neural Networks and Their Applications

learning approaches for the detection of renal, retinal, breast, skin, and dental abnormalities and more. Includes applications of neural;

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Artificial Intelligence Techniques for Medical Image Analysis

Automated image classification and segmentation techniques are becoming increasingly important in the current medical field for pathology;

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Cardiovascular Imaging and Image Analysis

This book covers the state-of-the-art approaches for automated non-invasive systems for early cardiovascular disease diagnosis. It includes;

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Digital Image Processing for Ophthalmology

Digital Retinal Images for Vessel Extraction (DRIVE) database were used to evaluate the performance of the methods. The results demonstrate high;

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Automated Diagnostics and Analytics for Buildings

automated fault detection and diagnostics to every system and piece of equipment in a facility, as well as for whole buildings, is imminent. The;

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Computer Vision and Machine Intelligence in Medical Image Analysis

, microscopy and pathology. The studies included mainly focus on the detection of events from biomedical signals.;

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Image Processing for Automated Diagnosis of Cardiac Diseases

Image Processing for Automated Diagnosis of Cardiac Diseases highlights current and emerging technologies for the automated;

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Optic disc detection in Fluorescein Angiography Images

Optic disc detection is an important task in retinal imaging as its significance justifies whether the retina is normal or abnormal. The;

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Suprachoroidal Space Interventions

This book provides a practical overview of pathology and interventions in the suprachoroidal space of the eye. Concise and well-structured;

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Surface Imaging for Biomedical Applications

it relates to the diagnosis, detection, and monitoring of skin conditions and disease. Written from an engineer's perspective, the book;

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Skin Cancer Segmentation using Neural Network

cancers. Accurate skin lesion segmentation is critical in automated skin cancer early detection and diagnosis. This book proposes methods like;

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Color Image Processing

imaging Object recognition and feature detection Facial and retinal image analysis Digital camera image processing Spectral and;

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Advances in Computational Techniques for Biomedical Image Analysis

such as image enhancement, detection of edges and objects, analysis of shape, quantification of texture and sharpness, and pattern analysis. It;

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Applications of Advanced Machine Intelligence in Computer Vision and Object Recognition

disease detection, adaptive learning, and automated image segmentation, this book is ideally designed for engineers, physicians, researchers;

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Applications of Advanced Machine Intelligence in Computer Vision and Object Recognition

disease detection, adaptive learning, and automated image segmentation, this book is ideally designed for engineers, physicians, researchers;

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The Handbook of Medical Image Perception and Techniques

wide range of topics including signal detection, image interpretation and advanced image analysis (e.g. deep learning) techniques for;

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