Content Based Retrieval Of Medical Images
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Content Based Retrieval Of Medical Images
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Author : Paulo Mazzoncini de Azevedo-Marques
language : en
Publisher: Springer Nature
Release Date : 2022-06-01
Content Based Retrieval Of Medical Images written by Paulo Mazzoncini de Azevedo-Marques and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-06-01 with Technology & Engineering categories.
Content-based image retrieval (CBIR) is the process of retrieval of images from a database that are similar to a query image, using measures derived from the images themselves, rather than relying on accompanying text or annotation. To achieve CBIR, the contents of the images need to be characterized by quantitative features; the features of the query image are compared with the features of each image in the database and images having high similarity with respect to the query image are retrieved and displayed. CBIR of medical images is a useful tool and could provide radiologists with assistance in the form of a display of relevant past cases. One of the challenging aspects of CBIR is to extract features from the images to represent their visual, diagnostic, or application-specific information content. In this book, methods are presented for preprocessing, segmentation, landmarking, feature extraction, and indexing of mammograms for CBIR. The preprocessing steps include anisotropic diffusion and the Wiener filter to remove noise and perform image enhancement. Techniques are described for segmentation of the breast and fibroglandular disk, including maximum entropy, a moment-preserving method, and Otsu's method. Image processing techniques are described for automatic detection of the nipple and the edge of the pectoral muscle via analysis in the Radon domain. By using the nipple and the pectoral muscle as landmarks, mammograms are divided into their internal, external, upper, and lower parts for further analysis. Methods are presented for feature extraction using texture analysis, shape analysis, granulometric analysis, moments, and statistical measures. The CBIR system presented provides options for retrieval using the Kohonen self-organizing map and the k-nearest-neighbor method. Methods are described for inclusion of expert knowledge to reduce the semantic gap in CBIR, including the query point movement method for relevance feedback (RFb). Analysis of performance is described in terms of precision, recall, and relevance-weighted precision of retrieval. Results of application to a clinical database of mammograms are presented, including the input of expert radiologists into the CBIR and RFb processes. Models are presented for integration of CBIR and computer-aided diagnosis (CAD) with a picture archival and communication system (PACS) for efficient workflow in a hospital. Table of Contents: Introduction to Content-based Image Retrieval / Mammography and CAD of Breast Cancer / Segmentation and Landmarking of Mammograms / Feature Extraction and Indexing of Mammograms / Content-based Retrieval of Mammograms / Integration of CBIR and CAD into Radiological Workflow
Ai Innovation In Medical Imaging Diagnostics
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Author : Kalaivani Anbarasan
language : en
Publisher: Medical Information Science Reference
Release Date : 2020
Ai Innovation In Medical Imaging Diagnostics written by Kalaivani Anbarasan and has been published by Medical Information Science Reference this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020 with categories.
"This book examines the application of artificial intelligence in medical imaging diagnostics"--
Challenges And Applications For Implementing Machine Learning In Computer Vision
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Author : Kashyap, Ramgopal
language : en
Publisher: IGI Global
Release Date : 2019-10-04
Challenges And Applications For Implementing Machine Learning In Computer Vision written by Kashyap, Ramgopal and has been published by IGI Global this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-10-04 with Computers categories.
Machine learning allows for non-conventional and productive answers for issues within various fields, including problems related to visually perceptive computers. Applying these strategies and algorithms to the area of computer vision allows for higher achievement in tasks such as spatial recognition, big data collection, and image processing. There is a need for research that seeks to understand the development and efficiency of current methods that enable machines to see. Challenges and Applications for Implementing Machine Learning in Computer Vision is a collection of innovative research that combines theory and practice on adopting the latest deep learning advancements for machines capable of visual processing. Highlighting a wide range of topics such as video segmentation, object recognition, and 3D modelling, this publication is ideally designed for computer scientists, medical professionals, computer engineers, information technology practitioners, industry experts, scholars, researchers, and students seeking current research on the utilization of evolving computer vision techniques.
Feature Extraction In Medical Image Retrieval
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Author : Aswini Kumar Samantaray
language : en
Publisher: Springer
Release Date : 2024-05-15
Feature Extraction In Medical Image Retrieval written by Aswini Kumar Samantaray and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-05-15 with Computers categories.
Medical imaging is fundamental to modern healthcare, and its widespread use has resulted in creation of image databases. These repositories contain images from a diverse range of modalities, multidimensional as well as co-aligned multimodality images. These image collections offer opportunity for evidence-based diagnosis, teaching, and research. Advances in medical image analysis over last two decades shows there are now many algorithms and ideas available that allow to address medical image analysis tasks in commercial solutions with sufficient performance in terms of accuracy, reliability and speed. Content-based image retrieval (CBIR) is an image search technique that complements the conventional text-based retrieval of images by using visual features, such as color, texture, and shape, as search criteria. This book emphasizes the design of wavelet filter-banks as efficient and effective feature descriptors for medical image retrieval. Firstly, a generalized novel design of a family of multiplier-free orthogonal wavelet filter-banks is presented. In this, the dyadic filter coefficients are obtained based on double-shifting orthogonality property with allowable deviation from original filter coefficients. Next, a low complex symmetric Daub-4 orthogonal wavelet filter-bank is presented. This is achieved by slightly altering the perfect reconstruction condition to make designed filter-bank symmetric and to obtain dyadic filter coefficients. In third contribution, the first dyadic Gabor wavelet filter-bank is presented based on slight alteration in orientation parameter without disturbing remaining Gabor wavelet parameters. In addition, a novel feature descriptor based on the design of adaptive Gabor wavelet filter-bank is presented. The use of Maximum likelihood estimation is suggested to measure the similarity between the feature vectors of heterogeneous medical images. The performance of the suggested methods is evaluated on three different publicly available databases namely NEMA, OASIS and EXACT09. The performance in terms of average retrieval precision, average retrieval recall and computational time are compared with well-known existing methods.
Medical Content Based Retrieval For Clinical Decision Support
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Author : Henning Mueller
language : en
Publisher: Springer
Release Date : 2012-02-21
Medical Content Based Retrieval For Clinical Decision Support written by Henning Mueller and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-02-21 with Computers categories.
This book constitutes the refereed proceedings of the Second MICCAI Workshop on Medical Content-Based Retrieval for Clinical Decision Support, MCBR-CBS 2011, held in Toronto, Canada, in September 2011. The 11 revised full papers presented together with 2 invited talks were carefully reviewed and selected from 17 submissions. The papers are divided on several topics on medical image retrieval with textual approaches, visual word based approaches, applications and multidimensional retrieval.
Medical Content Based Retrieval For Clinical Decision Support
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Author : Barbara Caputo
language : en
Publisher: Springer Science & Business Media
Release Date : 2010-02-15
Medical Content Based Retrieval For Clinical Decision Support written by Barbara Caputo and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2010-02-15 with Business & Economics categories.
This book constitutes the refereed proceedings of the first MICCAI Workshop on Medical Content-Based Retrieval for Clinical Decision Support, MCBR_CBS 2009, held in London, UK, in September 2009. The 10 revised full papers were carefully reviewed and selected from numerous submissions. The papers are divide on several topics on medical image retrieval, clinical decision making and multimodal fusion.
Medical Content Based Retrieval For Clinical Decision Support
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Author : Hayit Greenspan
language : en
Publisher: Springer
Release Date : 2013-02-20
Medical Content Based Retrieval For Clinical Decision Support written by Hayit Greenspan and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-02-20 with Computers categories.
This book constitutes the refereed proceedings of the Third MICCAI Workshop on Medical Content-Based Retrieval for Clinical Decision Support, MCBR-CBS 2012, held in Nice, France, in October 2012. The 10 revised full papers presented together with 2 invited talks were carefully reviewed and selected from 15 submissions. The papers are divided on several topics on image analysis of visual or multimodal medical data (X-ray, MRI, CT, echo videos, time series data), machine learning of disease correlations in visual or multimodal data, algorithms for indexing and retrieval of data from visual or multimodal medical databases, disease model-building and clinical decision support systems based on visual or multimodal analysis, algorithms for medical image retrieval or classification, systems of retrieval or classification using the ImageCLEF collection.
Artificial Intelligence For Maximizing Content Based Image Retrieval
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Author : Ma, Zongmin
language : en
Publisher: IGI Global
Release Date : 2009-01-31
Artificial Intelligence For Maximizing Content Based Image Retrieval written by Ma, Zongmin and has been published by IGI Global this book supported file pdf, txt, epub, kindle and other format this book has been release on 2009-01-31 with Computers categories.
Discusses major aspects of content-based image retrieval (CBIR) using current technologies and applications within the artificial intelligence (AI) field.
Medical Image Computing And Computer Assisted Intervention Miccai 2020
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Author : Anne L. Martel
language : en
Publisher: Springer Nature
Release Date : 2020-10-02
Medical Image Computing And Computer Assisted Intervention Miccai 2020 written by Anne L. Martel and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-10-02 with Computers categories.
The seven-volume set LNCS 12261, 12262, 12263, 12264, 12265, 12266, and 12267 constitutes the refereed proceedings of the 23rd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2020, held in Lima, Peru, in October 2020. The conference was held virtually due to the COVID-19 pandemic. The 542 revised full papers presented were carefully reviewed and selected from 1809 submissions in a double-blind review process. The papers are organized in the following topical sections: Part I: machine learning methodologies Part II: image reconstruction; prediction and diagnosis; cross-domain methods and reconstruction; domain adaptation; machine learning applications; generative adversarial networks Part III: CAI applications; image registration; instrumentation and surgical phase detection; navigation and visualization; ultrasound imaging; video image analysis Part IV: segmentation; shape models and landmark detection Part V: biological, optical, microscopic imaging; cell segmentation and stain normalization; histopathology image analysis; opthalmology Part VI: angiography and vessel analysis; breast imaging; colonoscopy; dermatology; fetal imaging; heart and lung imaging; musculoskeletal imaging Part VI: brain development and atlases; DWI and tractography; functional brain networks; neuroimaging; positron emission tomography
Biomedical Image Analysis And Mining Techniques For Improved Health Outcomes
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Author : Karâa, Wahiba Ben Abdessalem
language : en
Publisher: IGI Global
Release Date : 2015-11-03
Biomedical Image Analysis And Mining Techniques For Improved Health Outcomes written by Karâa, Wahiba Ben Abdessalem and has been published by IGI Global this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-11-03 with Medical categories.
Every second, users produce large amounts of image data from medical and satellite imaging systems. Image mining techniques that are capable of extracting useful information from image data are becoming increasingly useful, especially in medicine and the health sciences. Biomedical Image Analysis and Mining Techniques for Improved Health Outcomes addresses major techniques regarding image processing as a tool for disease identification and diagnosis, as well as treatment recommendation. Highlighting current research intended to advance the medical field, this publication is essential for use by researchers, advanced-level students, academicians, medical professionals, and technology developers. An essential addition to the reference material available in the field of medicine, this timely publication covers a range of applied research on data mining, image processing, computational simulation, data visualization, and image retrieval.