Computer Vision And Machine Intelligence In Medical Image Analysis

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Advanced Machine Vision Paradigms for Medical Image Analysis

Tapan K. Gandhi,Siddhartha Bhattacharyya,Sourav De,Debanjan Konar,Sandip Dey
Advanced Machine Vision Paradigms for Medical Image Analysis Book PDF

Author : Tapan K. Gandhi,Siddhartha Bhattacharyya,Sourav De,Debanjan Konar,Sandip Dey
Publisher : Academic Press
Page : 308 pages
File Size : 55,7 Mb
Release : 2020-08-11
Category : Computers
ISBN : 9780128192962

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Advanced Machine Vision Paradigms for Medical Image Analysis by Tapan K. Gandhi,Siddhartha Bhattacharyya,Sourav De,Debanjan Konar,Sandip Dey Pdf

Computer vision and machine intelligence paradigms are prominent in the domain of medical image applications, including computer assisted diagnosis, image guided radiation therapy, landmark detection, imaging genomics, and brain connectomics. Medical image analysis and understanding are daunting tasks owing to the massive influx of multi-modal medical image data generated during routine clinal practice. Advanced computer vision and machine intelligence approaches have been employed in recent years in the field of image processing and computer vision. However, due to the unstructured nature of medical imaging data and the volume of data produced during routine clinical processes, the applicability of these meta-heuristic algorithms remains to be investigated. Advanced Machine Vision Paradigms for Medical Image Analysis presents an overview of how medical imaging data can be analyzed to provide better diagnosis and treatment of disease. Computer vision techniques can explore texture, shape, contour and prior knowledge along with contextual information, from image sequence and 3D/4D information which helps with better human understanding. Many powerful tools have been developed through image segmentation, machine learning, pattern classification, tracking, and reconstruction to surface much needed quantitative information not easily available through the analysis of trained human specialists. The aim of the book is for medical imaging professionals to acquire and interpret the data, and for computer vision professionals to learn how to provide enhanced medical information by using computer vision techniques. The ultimate objective is to benefit patients without adding to already high healthcare costs. Explores major emerging trends in technology which are supporting the current advancement of medical image analysis with the help of computational intelligence Highlights the advancement of conventional approaches in the field of medical image processing Investigates novel techniques and reviews the state-of-the-art in the areas of machine learning, computer vision, soft computing techniques, as well as their applications in medical image analysis

Deep Learning for Medical Image Analysis

S. Kevin Zhou,Hayit Greenspan,Dinggang Shen
Deep Learning for Medical Image Analysis Book PDF

Author : S. Kevin Zhou,Hayit Greenspan,Dinggang Shen
Publisher : Academic Press
Page : 544 pages
File Size : 47,5 Mb
Release : 2023-12-01
Category : Computers
ISBN : 9780323858885

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Deep Learning for Medical Image Analysis by S. Kevin Zhou,Hayit Greenspan,Dinggang Shen Pdf

Deep Learning for Medical Image Analysis, Second Edition is a great learning resource for academic and industry researchers and graduate students taking courses on machine learning and deep learning for computer vision and medical image computing and analysis. Deep learning provides exciting solutions for medical image analysis problems and is a key method for future applications. This book gives a clear understanding of the principles and methods of neural network and deep learning concepts, showing how the algorithms that integrate deep learning as a core component are applied to medical image detection, segmentation, registration, and computer-aided analysis. · Covers common research problems in medical image analysis and their challenges · Describes the latest deep learning methods and the theories behind approaches for medical image analysis · Teaches how algorithms are applied to a broad range of application areas including cardiac, neural and functional, colonoscopy, OCTA applications and model assessment · Includes a Foreword written by Nicholas Ayache

Computer Vision and Machine Intelligence in Medical Image Analysis

Mousumi Gupta,Debanjan Konar,Siddhartha Bhattacharyya,Sambhunath Biswas
Computer Vision and Machine Intelligence in Medical Image Analysis Book PDF

Author : Mousumi Gupta,Debanjan Konar,Siddhartha Bhattacharyya,Sambhunath Biswas
Publisher : Springer Nature
Page : 150 pages
File Size : 40,7 Mb
Release : 2019-08-28
Category : Technology & Engineering
ISBN : 9789811387982

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Computer Vision and Machine Intelligence in Medical Image Analysis by Mousumi Gupta,Debanjan Konar,Siddhartha Bhattacharyya,Sambhunath Biswas Pdf

This book includes high-quality papers presented at the Symposium 2019, organised by Sikkim Manipal Institute of Technology (SMIT), in Sikkim from 26–27 February 2019. It discusses common research problems and challenges in medical image analysis, such as deep learning methods. It also discusses how these theories can be applied to a broad range of application areas, including lung and chest x-ray, breast CAD, microscopy and pathology. The studies included mainly focus on the detection of events from biomedical signals.

Advancement of Machine Intelligence in Interactive Medical Image Analysis

Om Prakash Verma,Sudipta Roy,Subhash Chandra Pandey,Mamta Mittal
Advancement of Machine Intelligence in Interactive Medical Image Analysis Book PDF

Author : Om Prakash Verma,Sudipta Roy,Subhash Chandra Pandey,Mamta Mittal
Publisher : Springer Nature
Page : 336 pages
File Size : 49,8 Mb
Release : 2019-12-11
Category : Computers
ISBN : 9789811511004

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Advancement of Machine Intelligence in Interactive Medical Image Analysis by Om Prakash Verma,Sudipta Roy,Subhash Chandra Pandey,Mamta Mittal Pdf

The book discusses major technical advances and research findings in the field of machine intelligence in medical image analysis. It examines the latest technologies and that have been implemented in clinical practice, such as computational intelligence in computer-aided diagnosis, biological image analysis, and computer-aided surgery and therapy. This book provides insights into the basic science involved in processing, analysing, and utilising all aspects of advanced computational intelligence in medical decision-making based on medical imaging.

Medical Imaging

K.C. Santosh,Sameer Antani,DS Guru,Nilanjan Dey
Medical Imaging Book PDF

Author : K.C. Santosh,Sameer Antani,DS Guru,Nilanjan Dey
Publisher : CRC Press
Page : 200 pages
File Size : 52,5 Mb
Release : 2019-08-20
Category : Computers
ISBN : 9780429639326

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Medical Imaging by K.C. Santosh,Sameer Antani,DS Guru,Nilanjan Dey Pdf

The book discusses varied topics pertaining to advanced or up-to-date techniques in medical imaging using artificial intelligence (AI), image recognition (IR) and machine learning (ML) algorithms/techniques. Further, coverage includes analysis of chest radiographs (chest x-rays) via stacked generalization models, TB type detection using slice separation approach, brain tumor image segmentation via deep learning, mammogram mass separation, epileptic seizures, breast ultrasound images, knee joint x-ray images, bone fracture detection and labeling, and diabetic retinopathy. It also reviews 3D imaging in biomedical applications and pathological medical imaging.

Medical Computer Vision

Bjoern Menze,Georg Langs,Zhuowen Tu,Antonio Criminisi
Medical Computer Vision Book PDF

Author : Bjoern Menze,Georg Langs,Zhuowen Tu,Antonio Criminisi
Publisher : Springer
Page : 226 pages
File Size : 50,6 Mb
Release : 2011-02-02
Category : Computers
ISBN : 9783642184215

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Medical Computer Vision by Bjoern Menze,Georg Langs,Zhuowen Tu,Antonio Criminisi Pdf

This book constitutes the thoroughly refereed post-workshop proceedings of the International Workshop on Medical Computer Vision, MCV 2010, held in Beijing, China, in September 2010 as a satellite event of the 13th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2010. The 10 revised full papers and 11 revised poster papers presented were carefully reviewed and selected from 38 initial submissions. The papers explore the use of modern image recognition technology in tasks such as semantic anatomy parsing, automatic segmentation and quantification, anomaly detection and categorization, data harvesting, semantic navigation and visualization, data organization and clustering, and general-purpose automatic understanding of medical images.

Computer Vision in Medical Imaging

Chi-hau Chen
Computer Vision in Medical Imaging Book PDF

Author : Chi-hau Chen
Publisher : World Scientific
Page : 410 pages
File Size : 47,8 Mb
Release : 2013-11-18
Category : Medical
ISBN : 9789814460941

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Computer Vision in Medical Imaging by Chi-hau Chen Pdf

The major progress in computer vision allows us to make extensive use of medical imaging data to provide us better diagnosis, treatment and predication of diseases. Computer vision can exploit texture, shape, contour and prior knowledge along with contextual information from image sequence and provide 3D and 4D information that helps with better human understanding. Many powerful tools have been available through image segmentation, machine learning, pattern classification, tracking, reconstruction to bring much needed quantitative information not easily available by trained human specialists. The aim of the book is for both medical imaging professionals to acquire and interpret the data, and computer vision professionals to provide enhanced medical information by using computer vision techniques. The final objective is to benefit the patients without adding to the already high medical costs.

Medical Image Analysis

Alejandro Frangi,Jerry Prince,Milan Sonka
Medical Image Analysis Book PDF

Author : Alejandro Frangi,Jerry Prince,Milan Sonka
Publisher : Academic Press
Page : 700 pages
File Size : 53,5 Mb
Release : 2023-09-20
Category : Technology & Engineering
ISBN : 9780128136584

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Medical Image Analysis by Alejandro Frangi,Jerry Prince,Milan Sonka Pdf

Medical Image Analysis presents practical knowledge on medical image computing and analysis as written by top educators and experts. This text is a modern, practical, self-contained reference that conveys a mix of fundamental methodological concepts within different medical domains. Sections cover core representations and properties of digital images and image enhancement techniques, advanced image computing methods (including segmentation, registration, motion and shape analysis), machine learning, how medical image computing (MIC) is used in clinical and medical research, and how to identify alternative strategies and employ software tools to solve typical problems in MIC. Provides an authoritative description of key concepts and methods Includes tutorial-based sections that clearly explain principles and their application to different medical domains Presents a representative selection of topics to match a modern and relevant approach to medical image computing

Understanding and Interpreting Machine Learning in Medical Image Computing Applications

Danail Stoyanov,Zeike Taylor,Seyed Mostafa Kia,Ipek Oguz,Mauricio Reyes,Anne Martel,Lena Maier-Hein,Andre F. Marquand,Edouard Duchesnay,Tommy Löfstedt,Bennett Landman,M. Jorge Cardoso,Carlos A. Silva,Sergio Pereira,Raphael Meier
Understanding and Interpreting Machine Learning in Medical Image Computing Applications Book PDF

Author : Danail Stoyanov,Zeike Taylor,Seyed Mostafa Kia,Ipek Oguz,Mauricio Reyes,Anne Martel,Lena Maier-Hein,Andre F. Marquand,Edouard Duchesnay,Tommy Löfstedt,Bennett Landman,M. Jorge Cardoso,Carlos A. Silva,Sergio Pereira,Raphael Meier
Publisher : Springer
Page : 149 pages
File Size : 41,9 Mb
Release : 2018-10-23
Category : Computers
ISBN : 9783030026288

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Understanding and Interpreting Machine Learning in Medical Image Computing Applications by Danail Stoyanov,Zeike Taylor,Seyed Mostafa Kia,Ipek Oguz,Mauricio Reyes,Anne Martel,Lena Maier-Hein,Andre F. Marquand,Edouard Duchesnay,Tommy Löfstedt,Bennett Landman,M. Jorge Cardoso,Carlos A. Silva,Sergio Pereira,Raphael Meier Pdf

This book constitutes the refereed joint proceedings of the First International Workshop on Machine Learning in Clinical Neuroimaging, MLCN 2018, the First International Workshop on Deep Learning Fails, DLF 2018, and the First International Workshop on Interpretability of Machine Intelligence in Medical Image Computing, iMIMIC 2018, held in conjunction with the 21st International Conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2018, in Granada, Spain, in September 2018. The 4 full MLCN papers, the 6 full DLF papers, and the 6 full iMIMIC papers included in this volume were carefully reviewed and selected. The MLCN contributions develop state-of-the-art machine learning methods such as spatio-temporal Gaussian process analysis, stochastic variational inference, and deep learning for applications in Alzheimer's disease diagnosis and multi-site neuroimaging data analysis; the DLF papers evaluate the strengths and weaknesses of DL and identify the main challenges in the current state of the art and future directions; the iMIMIC papers cover a large range of topics in the field of interpretability of machine learning in the context of medical image analysis.

Interpretability of Machine Intelligence in Medical Image Computing, and Topological Data Analysis and Its Applications for Medical Data

Mauricio Reyes,Pedro Henriques Abreu,Jaime Cardoso,Mustafa Hajij,Ghada Zamzmi,Paul Rahul,Lokendra Thakur
Interpretability of Machine Intelligence in Medical Image Computing and Topological Data Analysis and Its Applications for Medical Data Book PDF

Author : Mauricio Reyes,Pedro Henriques Abreu,Jaime Cardoso,Mustafa Hajij,Ghada Zamzmi,Paul Rahul,Lokendra Thakur
Publisher : Springer Nature
Page : 138 pages
File Size : 50,6 Mb
Release : 2021-09-21
Category : Computers
ISBN : 9783030874445

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Interpretability of Machine Intelligence in Medical Image Computing, and Topological Data Analysis and Its Applications for Medical Data by Mauricio Reyes,Pedro Henriques Abreu,Jaime Cardoso,Mustafa Hajij,Ghada Zamzmi,Paul Rahul,Lokendra Thakur Pdf

This book constitutes the refereed joint proceedings of the 4th International Workshop on Interpretability of Machine Intelligence in Medical Image Computing, iMIMIC 2020, and the First International Workshop on Topological Data Analysis and Its Applications for Medical Data, TDA4MedicalData 2021, held on September 27, 2021, in conjunction with the 24th International Conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2021. The 7 full papers presented at iMIMIC 2021 and 5 full papers held at TDA4MedicalData 2021 were carefully reviewed and selected from 12 submissions each. The iMIMIC papers focus on introducing the challenges and opportunities related to the topic of interpretability of machine learning systems in the context of medical imaging and computer assisted intervention. TDA4MedicalData is focusing on using TDA techniques to enhance the performance, generalizability, efficiency, and explainability of the current methods applied to medical data.

Interpretability of Machine Intelligence in Medical Image Computing and Multimodal Learning for Clinical Decision Support

Kenji Suzuki,Mauricio Reyes,Tanveer Syeda-Mahmood,Ender Konukoglu,Ben Glocker,Roland Wiest,Yaniv Gur,Hayit Greenspan,Anant Madabhushi
Interpretability of Machine Intelligence in Medical Image Computing and Multimodal Learning for Clinical Decision Support Book PDF

Author : Kenji Suzuki,Mauricio Reyes,Tanveer Syeda-Mahmood,Ender Konukoglu,Ben Glocker,Roland Wiest,Yaniv Gur,Hayit Greenspan,Anant Madabhushi
Publisher : Springer Nature
Page : 93 pages
File Size : 40,9 Mb
Release : 2019-10-24
Category : Computers
ISBN : 9783030338503

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Interpretability of Machine Intelligence in Medical Image Computing and Multimodal Learning for Clinical Decision Support by Kenji Suzuki,Mauricio Reyes,Tanveer Syeda-Mahmood,Ender Konukoglu,Ben Glocker,Roland Wiest,Yaniv Gur,Hayit Greenspan,Anant Madabhushi Pdf

This book constitutes the refereed joint proceedings of the Second International Workshop on Interpretability of Machine Intelligence in Medical Image Computing, iMIMIC 2019, and the 9th International Workshop on Multimodal Learning for Clinical Decision Support, ML-CDS 2019, held in conjunction with the 22nd International Conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2019, in Shenzhen, China, in October 2019. The 7 full papers presented at iMIMIC 2019 and the 3 full papers presented at ML-CDS 2019 were carefully reviewed and selected from 10 submissions to iMIMIC and numerous submissions to ML-CDS. The iMIMIC papers focus on introducing the challenges and opportunities related to the topic of interpretability of machine learning systems in the context of medical imaging and computer assisted intervention. The ML-CDS papers discuss machine learning on multimodal data sets for clinical decision support and treatment planning.

Artificial Intelligence and Machine Learning in 2D/3D Medical Image Processing

Rohit Raja,Sandeep Kumar,Shilpa Rani,K. Ramya Laxmi
Artificial Intelligence and Machine Learning in 2D 3D Medical Image Processing Book PDF

Author : Rohit Raja,Sandeep Kumar,Shilpa Rani,K. Ramya Laxmi
Publisher : CRC Press
Page : 215 pages
File Size : 46,9 Mb
Release : 2020-12-22
Category : Medical
ISBN : 9781000337075

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Artificial Intelligence and Machine Learning in 2D/3D Medical Image Processing by Rohit Raja,Sandeep Kumar,Shilpa Rani,K. Ramya Laxmi Pdf

Digital images have several benefits, such as faster and inexpensive processing cost, easy storage and communication, immediate quality assessment, multiple copying while preserving quality, swift and economical reproduction, and adaptable manipulation. Digital medical images play a vital role in everyday life. Medical imaging is the process of producing visible images of inner structures of the body for scientific and medical study and treatment as well as a view of the function of interior tissues. This process pursues disorder identification and management. Medical imaging in 2D and 3D includes many techniques and operations such as image gaining, storage, presentation, and communication. The 2D and 3D images can be processed in multiple dimensions. Depending on the requirement of a specific problem, one must identify various features of 2D or 3D images while applying suitable algorithms. These image processing techniques began in the 1960s and were used in such fields as space, clinical purposes, the arts, and television image improvement. In the 1970s, with the development of computer systems, the cost of image processing was reduced and processes became faster. In the 2000s, image processing became quicker, inexpensive, and simpler. In the 2020s, image processing has become a more accurate, more efficient, and self-learning technology. This book highlights the framework of the robust and novel methods for medical image processing techniques in 2D and 3D. The chapters explore existing and emerging image challenges and opportunities in the medical field using various medical image processing techniques. The book discusses real-time applications for artificial intelligence and machine learning in medical image processing. The authors also discuss implementation strategies and future research directions for the design and application requirements of these systems. This book will benefit researchers in the medical image processing field as well as those looking to promote the mutual understanding of researchers within different disciplines that incorporate AI and machine learning. FEATURES Highlights the framework of robust and novel methods for medical image processing techniques Discusses implementation strategies and future research directions for the design and application requirements of medical imaging Examines real-time application needs Explores existing and emerging image challenges and opportunities in the medical field

Machine Learning in Medical Imaging and Computer Vision

Amita Nandal,Liang Zhou,Arvind Dhaka,Todor Ganchev,Farid Nait-Abdesselam
Machine Learning in Medical Imaging and Computer Vision Book PDF

Author : Amita Nandal,Liang Zhou,Arvind Dhaka,Todor Ganchev,Farid Nait-Abdesselam
Publisher : IET
Page : 381 pages
File Size : 50,5 Mb
Release : 2024-01-30
Category : Computers
ISBN : 9781839535932

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Machine Learning in Medical Imaging and Computer Vision by Amita Nandal,Liang Zhou,Arvind Dhaka,Todor Ganchev,Farid Nait-Abdesselam Pdf

This edited book explores new and emerging technologies in the field of medical image processing using deep learning models, neural networks and machine learning architectures. Multimodal medical imaging and optimisation techniques are discussed in relation to the advances, challenges and benefits of computer-aided diagnoses.

Computational Intelligence in Medical Imaging

G. Schaefer,A. Hassanien,J. Jiang
Computational Intelligence in Medical Imaging Book PDF

Author : G. Schaefer,A. Hassanien,J. Jiang
Publisher : CRC Press
Page : 504 pages
File Size : 49,7 Mb
Release : 2009-03-24
Category : Computers
ISBN : 1420060619

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Computational Intelligence in Medical Imaging by G. Schaefer,A. Hassanien,J. Jiang Pdf

CI Techniques & Algorithms for a Variety of Medical Imaging Situations Documents recent advances and stimulates further research A compilation of the latest trends in the field, Computational Intelligence in Medical Imaging: Techniques and Applications explores how intelligent computing can bring enormous benefit to existing technology in medical image processing as well as improve medical imaging research. The contributors also cover state-of-the-art research toward integrating medical image processing with artificial intelligence and machine learning approaches. The book presents numerous techniques, algorithms, and models. It describes neural networks, evolutionary optimization techniques, rough sets, support vector machines, tabu search, fuzzy logic, a Bayesian probabilistic framework, a statistical parts-based appearance model, a reinforcement learning-based multistage image segmentation algorithm, a machine learning approach, Monte Carlo simulations, and intelligent, deformable models. The contributors discuss how these techniques are used to classify wound images, extract the boundaries of skin lesions, analyze prostate cancer, handle the inherent uncertainties in mammographic images, and encapsulate the natural intersubject anatomical variance in medical images. They also examine prostate segmentation in transrectal ultrasound images, automatic segmentation and diagnosis of bone scintigraphy, 3-D medical image segmentation, and the reconstruction of SPECT and PET tomographic images.

Artificial Intelligence in Medical Imaging

Lia Morra,Silvia Delsanto,Loredana Correale
Artificial Intelligence in Medical Imaging Book PDF

Author : Lia Morra,Silvia Delsanto,Loredana Correale
Publisher : CRC Press
Page : 165 pages
File Size : 50,5 Mb
Release : 2019-11-25
Category : Science
ISBN : 9781000753080

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Artificial Intelligence in Medical Imaging by Lia Morra,Silvia Delsanto,Loredana Correale Pdf

Choice Recommended Title, January 2021 This book, written by authors with more than a decade of experience in the design and development of artificial intelligence (AI) systems in medical imaging, will guide readers in the understanding of one of the most exciting fields today. After an introductory description of classical machine learning techniques, the fundamentals of deep learning are explained in a simple yet comprehensive manner. The book then proceeds with a historical perspective of how medical AI developed in time, detailing which applications triumphed and which failed, from the era of computer aided detection systems on to the current cutting-edge applications in deep learning today, which are starting to exhibit on-par performance with clinical experts. In the last section, the book offers a view on the complexity of the validation of artificial intelligence applications for commercial use, describing the recently introduced concept of software as a medical device, as well as good practices and relevant considerations for training and testing machine learning systems for medical use. Open problematics on the validation for public use of systems which by nature continuously evolve through new data is also explored. The book will be of interest to graduate students in medical physics, biomedical engineering and computer science, in addition to researchers and medical professionals operating in the medical imaging domain, who wish to better understand these technologies and the future of the field. Features: An accessible yet detailed overview of the field Explores a hot and growing topic Provides an interdisciplinary perspective