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Automatic Image Segmentation Based On Level Set Approach Application To Brain Tumor Segmentation In Mr Images
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Book Synopsis Automatic image segmentation based on level set approach: application to brain tumor segmentation in MR images by : Xiaobing Li
Download or read book Automatic image segmentation based on level set approach: application to brain tumor segmentation in MR images written by Xiaobing Li and published by . This book was released on 2009 with total page 119 pages. Available in PDF, EPUB and Kindle. Book excerpt: The aim of this dissertation is to develop an automatic segmentation of brain tumors from MRI volume based on the technique of "level sets". The term "automatic" uses the fact that the normal brain is symmetrical and the localization of asymmetrical regions permits to estimate the initial contour of the tumor. The first step is preprocessing, which is to correct the intensity inhomogeneity of volume MRI and spatially realign the MRI volumes of the same patient at different moments. The plan hemispherical brain is then calculated by maximizing the degree of similarity between the half of the volume and his reflexion. The initial contour of the tumor can be extracted from the asymmetry between the two hemispheres. This initial contour is evolved and refined by the technique "level set" in order to find the real contour of the tumor. The criteria for stopping the evolution have been proposed and based on the properties of the tumor. Finally, the contour of the tumor is projected onto the adjacent images to form the new initial contours. This process is iterated on all slices to obtain the segmentation of the tumor in 3D. The proposed system is used to follow up patients throughout the medical treatment period, with examinations every four months, allowing the physician to monitor the state of development of the tumor and evaluate the effectiveness of the therapy. The method was quantitatively evaluated by comparison with manual tracings experts. Good results are obtained on real MRI images.
Book Synopsis Brain Tumor MRI Image Segmentation Using Deep Learning Techniques by : Jyotismita Chaki
Download or read book Brain Tumor MRI Image Segmentation Using Deep Learning Techniques written by Jyotismita Chaki and published by Academic Press. This book was released on 2021-11-27 with total page 260 pages. Available in PDF, EPUB and Kindle. Book excerpt: Brain Tumor MRI Image Segmentation Using Deep Learning Techniques offers a description of deep learning approaches used for the segmentation of brain tumors. The book demonstrates core concepts of deep learning algorithms by using diagrams, data tables and examples to illustrate brain tumor segmentation. After introducing basic concepts of deep learning-based brain tumor segmentation, sections cover techniques for modeling, segmentation and properties. A focus is placed on the application of different types of convolutional neural networks, like single path, multi path, fully convolutional network, cascade convolutional neural networks, Long Short-Term Memory - Recurrent Neural Network and Gated Recurrent Units, and more. The book also highlights how the use of deep neural networks can address new questions and protocols, as well as improve upon existing challenges in brain tumor segmentation. Provides readers with an understanding of deep learning-based approaches in the field of brain tumor segmentation, including preprocessing techniques Integrates recent advancements in the field, including the transformation of low-resolution brain tumor images into super-resolution images using deep learning-based methods, single path Convolutional Neural Network based brain tumor segmentation, and much more Includes coverage of Long Short-Term Memory (LSTM) based Recurrent Neural Network (RNN), Gated Recurrent Units (GRU) based Recurrent Neural Network (RNN), Generative Adversarial Networks (GAN), Auto Encoder based brain tumor segmentation, and Ensemble deep learning Model based brain tumor segmentation Covers research Issues and the future of deep learning-based brain tumor segmentation
Author :Management Association, Information Resources Publisher :IGI Global ISBN 13 :1522505725 Total Pages :2118 pages Book Rating :4.5/5 (225 download)
Book Synopsis Medical Imaging: Concepts, Methodologies, Tools, and Applications by : Management Association, Information Resources
Download or read book Medical Imaging: Concepts, Methodologies, Tools, and Applications written by Management Association, Information Resources and published by IGI Global. This book was released on 2016-07-18 with total page 2118 pages. Available in PDF, EPUB and Kindle. Book excerpt: Medical imaging has transformed the ways in which various conditions, injuries, and diseases are identified, monitored, and treated. As various types of digital visual representations continue to advance and improve, new opportunities for their use in medical practice will likewise evolve. Medical Imaging: Concepts, Methodologies, Tools, and Applications presents a compendium of research on digital imaging technologies in a variety of healthcare settings. This multi-volume work contains practical examples of implementation, emerging trends, case studies, and technological innovations essential for using imaging technologies for making medical decisions. This comprehensive publication is an essential resource for medical practitioners, digital imaging technologists, researchers, and medical students.
Book Synopsis Image Segmentation and Level Set Method by : Jérôme Piovano
Download or read book Image Segmentation and Level Set Method written by Jérôme Piovano and published by . This book was released on 2009 with total page 159 pages. Available in PDF, EPUB and Kindle. Book excerpt: Magnetic Resonance Images (MRI) have been introduced at the end of the XXth century and have revolutionized the world of modern medicine, allowing to view with precision the inside of anatomical structures in a non-invasive way. This imaging technique has greatly contributed to the study and comprehension of the human brain, allowing to discern with precision the different anatomical structures composing the head, especially the cerebral cortex. Discernment between these anatomical structures is called segmentation, and consist in "extracting" structures of interest from MRIs. Several models exists to perform image segmentation, and this thesis focus on those based on hypersurface evolutions: an hypersurface (surface in 3D) is incrementally adjusted to finally fit the border of the region of interest. A head model corresponds to the partitioning of the head into several segmented anatomical structures. A classic head model generally includes 5 anatomical structures (skin, skull, cerebrospinal fluid, grey matter, white matter), nested inside each other in the manner of "Russian nested dolls". Nevertheless because of the complexity of their shapes, manual segmentation of these structures is tedious and extremely difficult.This thesis is dedicated to the creation of new segmentation models robust to MRI alterations, and to the application of these models in the purpose of automatic creation of anatomical head models. After briefly reviewing some classical models in image segmentation, two contributions to segmentation based on hypersurface evolution are proposed. The first one corresponds to a new representation and a new numerical scheme for the level-sets method, based on quadrilateral finite elements. This representation aims at improving the accuracy and robustness of the model. The second contribution corresponds to a new segmentation model based on local statistics, and robust to standard MRI alterations. This model aims at unifying several 'state-of-the-art' models in image segmentation. Finally, a framework for automatic creation of anatomical head models is proposed, mainly using the previous local-statistic based segmentation model.
Book Synopsis Level Set Method in Medical Imaging Segmentation by : Ayman El-Baz
Download or read book Level Set Method in Medical Imaging Segmentation written by Ayman El-Baz and published by CRC Press. This book was released on 2019-06-26 with total page 300 pages. Available in PDF, EPUB and Kindle. Book excerpt: Level set methods are numerical techniques which offer remarkably powerful tools for understanding, analyzing, and computing interface motion in a host of settings. When used for medical imaging analysis and segmentation, the function assigns a label to each pixel or voxel and optimality is defined based on desired imaging properties. This often includes a detection step to extract specific objects via segmentation. This allows for the segmentation and analysis problem to be formulated and solved in a principled way based on well-established mathematical theories. Level set method is a great tool for modeling time varying medical images and enhancement of numerical computations.
Book Synopsis Quantitative MRI in Cancer by : Thomas E. Yankeelov
Download or read book Quantitative MRI in Cancer written by Thomas E. Yankeelov and published by Taylor & Francis. This book was released on 2011-09-13 with total page 331 pages. Available in PDF, EPUB and Kindle. Book excerpt: Propelling quantitative MRI techniques from bench to bedside, Quantitative MRI in Cancer presents a range of quantitative MRI methods for assessing tumor biology. It includes biophysical and theoretical explanations of the most relevant MRI techniques as well as examples of these techniques in cancer applications.The introductory part of the book c
Book Synopsis Variational and Level Set Methods in Image Segmentation by : Amar Mitiche
Download or read book Variational and Level Set Methods in Image Segmentation written by Amar Mitiche and published by Springer Science & Business Media. This book was released on 2010-10-22 with total page 192 pages. Available in PDF, EPUB and Kindle. Book excerpt: Image segmentation consists of dividing an image domain into disjoint regions according to a characterization of the image within or in-between the regions. Therefore, segmenting an image is to divide its domain into relevant components. The efficient solution of the key problems in image segmentation promises to enable a rich array of useful applications. The current major application areas include robotics, medical image analysis, remote sensing, scene understanding, and image database retrieval. The subject of this book is image segmentation by variational methods with a focus on formulations which use closed regular plane curves to define the segmentation regions and on a level set implementation of the corresponding active curve evolution algorithms. Each method is developed from an objective functional which embeds constraints on both the image domain partition of the segmentation and the image data within or in-between the partition regions. The necessary conditions to optimize the objective functional are then derived and solved numerically. The book covers, within the active curve and level set formalism, the basic two-region segmentation methods, multiregion extensions, region merging, image modeling, and motion based segmentation. To treat various important classes of images, modeling investigates several parametric distributions such as the Gaussian, Gamma, Weibull, and Wishart. It also investigates non-parametric models. In motion segmentation, both optical flow and the movement of real three-dimensional objects are studied.
Book Synopsis Neutrosophic Set in Medical Image Analysis by : Yanhui Guo
Download or read book Neutrosophic Set in Medical Image Analysis written by Yanhui Guo and published by Academic Press. This book was released on 2019-08-08 with total page 370 pages. Available in PDF, EPUB and Kindle. Book excerpt: Neutrosophic Set in Medical Image Analysis gives an understanding of the concepts of NS, along with knowledge on how to gather, interpret, analyze and handle medical images using NS methods. It presents the latest cutting-edge research that gives insight into neutrosophic set’s novel techniques, strategies and challenges, showing how it can be used in biomedical diagnoses systems. The neutrosophic set (NS), which is a generalization of fuzzy set, offers the prospect of overcoming the restrictions of fuzzy-based approaches to medical image analysis. Introduces the mathematical model and concepts of neutrosophic theory and methods Highlights the different techniques of neutrosophic theory, focusing on applying the neutrosophic set in image analysis to support computer- aided diagnosis (CAD) systems, including approaches from soft computing and machine learning Shows how NS techniques can be applied to medical image denoising, segmentation and classification Provides challenges and future directions in neutrosophic set based medical image analysis
Book Synopsis Diagnostic Techniques and Surgical Management of Brain Tumors by : Ana Lucia Abujamra
Download or read book Diagnostic Techniques and Surgical Management of Brain Tumors written by Ana Lucia Abujamra and published by IntechOpen. This book was released on 2011-09-22 with total page 546 pages. Available in PDF, EPUB and Kindle. Book excerpt: The focus of the book Diagnostic Techniques and Surgical Management of Brain Tumors is on describing the established and newly-arising techniques to diagnose central nervous system tumors, with a special focus on neuroimaging, followed by a discussion on the neurosurgical guidelines and techniques to manage and treat this disease. Each chapter in the Diagnostic Techniques and Surgical Management of Brain Tumors is authored by international experts with extensive experience in the areas covered.
Book Synopsis Multi Modality State-of-the-Art Medical Image Segmentation and Registration Methodologies by : Ayman S. El-Baz
Download or read book Multi Modality State-of-the-Art Medical Image Segmentation and Registration Methodologies written by Ayman S. El-Baz and published by Springer Science & Business Media. This book was released on 2011-04-11 with total page 369 pages. Available in PDF, EPUB and Kindle. Book excerpt: With the advances in image guided surgery for cancer treatment, the role of image segmentation and registration has become very critical. The central engine of any image guided surgery product is its ability to quantify the organ or segment the organ whether it is a magnetic resonance imaging (MRI) and computed tomography (CT), X-ray, PET, SPECT, Ultrasound, and Molecular imaging modality. Sophisticated segmentation algorithms can help the physicians delineate better the anatomical structures present in the input images, enhance the accuracy of medical diagnosis and facilitate the best treatment planning system designs. The focus of this book in towards the state of the art techniques in the area of image segmentation and registration.
Book Synopsis MRI Brain Tumor Segmentation Methods - A Review by : Gursangeet Kaur
Download or read book MRI Brain Tumor Segmentation Methods - A Review written by Gursangeet Kaur and published by Infinite Study. This book was released on with total page 5 pages. Available in PDF, EPUB and Kindle. Book excerpt: Medical image processing and its segmentation is an active and interesting area for researchers. It has reached at the tremendous place in diagnosing tumors after the discovery of CT and MRI. MRI is an useful tool to detect the brain tumor and segmentation is performed to carry out the useful portion from an image. The purpose of this paper is to provide an overview of different image segmentation methods like watershed algorithm, morphological operations, neutrosophic sets, thresholding, K-means clustering, fuzzy C-means etc using MR images.
Book Synopsis Computational Intelligence in Biomedical Imaging by : Kenji Suzuki
Download or read book Computational Intelligence in Biomedical Imaging written by Kenji Suzuki and published by Springer Science & Business Media. This book was released on 2013-11-19 with total page 410 pages. Available in PDF, EPUB and Kindle. Book excerpt: Computational Intelligence in Biomedical Imaging is a comprehensive overview of the state-of-the-art computational intelligence research and technologies in biomedical images with emphasis on biomedical decision making. Biomedical imaging offers useful information on patients’ medical conditions and clues to causes of their symptoms and diseases. Biomedical images, however, provide a large number of images which physicians must interpret. Therefore, computer aids are demanded and become indispensable in physicians’ decision making. This book discusses major technical advancements and research findings in the field of computational intelligence in biomedical imaging, for example, computational intelligence in computer-aided diagnosis for breast cancer, prostate cancer, and brain disease, in lung function analysis, and in radiation therapy. The book examines technologies and studies that have reached the practical level, and those technologies that are becoming available in clinical practices in hospitals rapidly such as computational intelligence in computer-aided diagnosis, biological image analysis, and computer-aided surgery and therapy.
Book Synopsis Hybrid Computational Intelligent Systems by : Siddhartha Bhattacharyya
Download or read book Hybrid Computational Intelligent Systems written by Siddhartha Bhattacharyya and published by CRC Press. This book was released on 2023-05-03 with total page 397 pages. Available in PDF, EPUB and Kindle. Book excerpt: Hybrid Computational Intelligent Systems – Modeling, Simulation and Optimization unearths the latest advances in evolving hybrid intelligent modeling and simulation of human-centric data-intensive applications optimized for real-time use, thereby enabling researchers to come up with novel breakthroughs in this ever-growing field. Salient features include the fundamentals of modeling and simulation with recourse to knowledge-based simulation, interaction paradigms, and human factors, along with the enhancement of the existing state of art in a high-performance computing setup. In addition, this book presents optimization strategies to evolve robust and failsafe intelligent system modeling and simulation. The volume also highlights novel applications for different engineering problems including signal and data processing, speech, image, sensor data processing, innovative intelligent systems, and swarm intelligent manufacturing systems. Features: A self-contained approach to integrating the principles of hybrid computational ntelligence with system modeling and simulation Well-versed foundation of computational intelligence and its application to real life engineering problems Elucidates essential background, concepts, definitions, and theories thereby putting forward a complete treatment on the subject Effective modeling of hybrid intelligent systems forms the backbone of almost every operative system in real-life Proper simulation of real-time hybrid intelligent systems is a prerequisite for deriving any real-life system solution Optimized system modeling and simulation enable real-time and failsafe operations of the existing hybrid intelligent system solutions Information presented in an accessible way for researchers, engineers, developers, and practitioners from academia and industry working in all major areas and interdisciplinary areas of hybrid computational intelligence and communication systems to evolve human-centered modeling and simulations of real-time data-intensive intelligent systems.
Book Synopsis Deep Learning and Parallel Computing Environment for Bioengineering Systems by : Arun Kumar Sangaiah
Download or read book Deep Learning and Parallel Computing Environment for Bioengineering Systems written by Arun Kumar Sangaiah and published by Academic Press. This book was released on 2019-07-26 with total page 280 pages. Available in PDF, EPUB and Kindle. Book excerpt: Deep Learning and Parallel Computing Environment for Bioengineering Systems delivers a significant forum for the technical advancement of deep learning in parallel computing environment across bio-engineering diversified domains and its applications. Pursuing an interdisciplinary approach, it focuses on methods used to identify and acquire valid, potentially useful knowledge sources. Managing the gathered knowledge and applying it to multiple domains including health care, social networks, mining, recommendation systems, image processing, pattern recognition and predictions using deep learning paradigms is the major strength of this book. This book integrates the core ideas of deep learning and its applications in bio engineering application domains, to be accessible to all scholars and academicians. The proposed techniques and concepts in this book can be extended in future to accommodate changing business organizations’ needs as well as practitioners’ innovative ideas. Presents novel, in-depth research contributions from a methodological/application perspective in understanding the fusion of deep machine learning paradigms and their capabilities in solving a diverse range of problems Illustrates the state-of-the-art and recent developments in the new theories and applications of deep learning approaches applied to parallel computing environment in bioengineering systems Provides concepts and technologies that are successfully used in the implementation of today's intelligent data-centric critical systems and multi-media Cloud-Big data
Book Synopsis Innovative theranostic approaches towards neuro-immunology in gliomas by : Lijie Zhai
Download or read book Innovative theranostic approaches towards neuro-immunology in gliomas written by Lijie Zhai and published by Frontiers Media SA. This book was released on 2023-04-05 with total page 225 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Hybrid Image Processing Methods for Medical Image Examination by : Venkatesan Rajinikanth
Download or read book Hybrid Image Processing Methods for Medical Image Examination written by Venkatesan Rajinikanth and published by CRC Press. This book was released on 2021-01-29 with total page 201 pages. Available in PDF, EPUB and Kindle. Book excerpt: In view of better results expected from examination of medical datasets (images) with hybrid (integration of thresholding and segmentation) image processing methods, this work focuses on implementation of possible hybrid image examination techniques for medical images. It describes various image thresholding and segmentation methods which are essential for the development of such a hybrid processing tool. Further, this book presents the essential details, such as test image preparation, implementation of a chosen thresholding operation, evaluation of threshold image, and implementation of segmentation procedure and its evaluation, supported by pertinent case studies. Aimed at researchers/graduate students in the medical image processing domain, image processing, and computer engineering, this book: Provides broad background on various image thresholding and segmentation techniques Discusses information on various assessment metrics and the confusion matrix Proposes integration of the thresholding technique with the bio-inspired algorithms Explores case studies including MRI, CT, dermoscopy, and ultrasound images Includes separate chapters on machine learning and deep learning for medical image processing
Book Synopsis Intraoperative Imaging and Image-Guided Therapy by : Ferenc A. Jolesz
Download or read book Intraoperative Imaging and Image-Guided Therapy written by Ferenc A. Jolesz and published by Springer Science & Business Media. This book was released on 2014-01-14 with total page 870 pages. Available in PDF, EPUB and Kindle. Book excerpt: Image-guided therapy (IGT) uses imaging to improve the localization and targeting of diseased tissue and to monitor and control treatments. During the past decade, image-guided surgeries and image-guided minimally invasive interventions have emerged as advances that can be used in place of traditional invasive approaches. Advanced imaging technologies such as magnetic resonance imaging (MRI), computed tomography (CT), and positron emission tomography (PET) entered into operating rooms and interventional suites to complement already-available routine imaging devices like X-ray and ultrasound. At the same time, navigational tools, computer-assisted surgery devices, and image-guided robots also became part of the revolution in interventional radiology suites and the operating room. Intraoperative Imaging and Image-Guided Therapy explores the fundamental, technical, and clinical aspects of state-of the-art image-guided therapies. It presents the basic concepts of image guidance, the technologies involved in therapy delivery, and the special requirements for the design and construction of image-guided operating rooms and interventional suites. It also covers future developments such as molecular imaging-guided surgeries and novel innovative therapies like MRI-guided focused ultrasound surgery. IGT is a multidisciplinary and multimodality field in which teams of physicians, physicists, engineers, and computer scientists collaborate in performing these interventions, an approach that is reflected in the organization of the book. Contributing authors include members of the National Center of Image-Guided Therapy program at Brigham and Women’s Hospital and international leaders in the field of IGT. The book includes coverage of these topics: - Imaging methods, guidance technologies, and the therapy delivery systems currently used or in development. - Clinical applications for IGT in various specialties such as neurosurgery, ear-nose-and-throat surgery, cardiovascular surgery, endoscopies, and orthopedic procedures. - Review and comparison of the clinical uses for IGT with conventional methods in terms of invasiveness, effectiveness, and outcome. - Requirements for the design and construction of image-guided operating rooms and interventional suites.
