Download or read book Efficient Multi modal Image Registration Based on Gradient Orientations of Minimal Uncertainty written by Dante De Nigris Moreno and published by . This book was released on 2015 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: "This thesis presents a general framework for the registration of medicalimages across multiple clinical contexts involving rigid and non-rigidapplications. The proposed framework relies on gradient orientations asprimitive geometric descriptors so as to locally assess image similarity basedon orientation alignment and evaluates the metric on sparse locationscorresponding to anatomical boundaries of interest. The two main advantagesbrought forward by the proposed approach are: (1) a substantial reduction incomputational complexity and processing time and (2) a significant improvementin robustness against multi-modal contexts with widely different imageformation models and significant non-homogeneities.The proposed approach is evaluated in multiple clinical contexts and comparedagainst state-of-the-art techniques. In the context of neurosurgery, imageregistration can be employed so as to update a pre-operative magneticresonance image (MRI) based on an intra-operative ultrasound volume. Theproposed approach is evaluated in this challenging time-sensitive scenario andis shown to provide robust performance with sub-second processing times. Inthe context of the rigid registration of computational tomography (CT) and MRIbrain volumes, the proposed approach is evaluated with a publicly availabledataset and compared against previously proposed techniques. The quantitativeresults demonstrate that the proposed approach can employ highly reducedsampling rates (e.g. only 0.05% of the voxels in the image) while stillyielding a median registration error inferior to 1mm. In the context of thenon-rigid registration of inter-patient MRI brain volumes, the proposedapproach is evaluated with a publicly available dataset which measuresregistration accuracy in terms of the agreement of spatially mapped labelswith expert annotated labels. The use of such dataset allows for a fair andunbiased comparison with over fourteen competing techniques. The quantitativeresults show that the proposed approach achieves slightly inferior accuracythan the top performing method but with only one sixth of the processing timerequired by the alternative technique. Finally, the proposed approach isevaluated in the context of automatic brain lesion detection which relies onhealthy tissue probability maps obtained via registration to a brain atlas.The quantitative comparison against two leading image registration techniquesshows that the proposed approach can lead to a slightly improved performanceof brain lesion detection algorithms while requiring only one sixth of theprocessing time used by competing registration approaches." --

Download or read book Medical Image Computing and Computer Assisted Intervention MICCAI 2016 written by Sebastien Ourselin and published by Springer. This book was released on 2016-10-17 with total page 666 pages. Available in PDF, EPUB and Kindle. Book excerpt: The three-volume set LNCS 9900, 9901, and 9902 constitutes the refereed proceedings of the 19th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2016, held in Athens, Greece, in October 2016. Based on rigorous peer reviews, the program committee carefully selected 228 revised regular papers from 756 submissions for presentation in three volumes. The papers have been organized in the following topical sections: Part I: brain analysis, brain analysis - connectivity; brain analysis - cortical morphology; Alzheimer disease; surgical guidance and tracking; computer aided interventions; ultrasound image analysis; cancer image analysis; Part II: machine learning and feature selection; deep learning in medical imaging; applications of machine learning; segmentation; cell image analysis; Part III: registration and deformation estimation; shape modeling; cardiac and vascular image analysis; image reconstruction; and MR image analysis.

Download or read book Image Video and 3D Data Registration written by Vasileios Argyriou and published by John Wiley & Sons. This book was released on 2015-07-01 with total page 247 pages. Available in PDF, EPUB and Kindle. Book excerpt: Data registration refers to a series of techniques for matching or bringing similar objects or datasets together into alignment. These techniques enjoy widespread use in a diverse variety of applications, such as video coding, tracking, object and face detection and recognition, surveillance and satellite imaging, medical image analysis and structure from motion. Registration methods are as numerous as their manifold uses, from pixel level and block or feature based methods to Fourier domain methods. This book is focused on providing algorithms and image and video techniques for registration and quality performance metrics. The authors provide various assessment metrics for measuring registration quality alongside analyses of registration techniques, introducing and explaining both familiar and state-of-the-art registration methodologies used in a variety of targeted applications. Key features: Provides a state-of-the-art review of image and video registration techniques, allowing readers to develop an understanding of how well the techniques perform by using specific quality assessment criteria Addresses a range of applications from familiar image and video processing domains to satellite and medical imaging among others, enabling readers to discover novel methodologies with utility in their own research Discusses quality evaluation metrics for each application domain with an interdisciplinary approach from different research perspectives

Download or read book Robust and Effective Techniques for Multi modal Image Registration written by Guohua Lv and published by . This book was released on 2015 with total page 320 pages. Available in PDF, EPUB and Kindle. Book excerpt: Image registration is the process of estimating the optimal transformation that aligns different imaging data into spatial correspondences. Multi-modal image registration is to register images which are captured by different types of imaging devices. This thesis aims to develop robust and effective techniques for multi-modal image registration. The challenge lies in the fact that the visual appearance may differ a lot between corresponding parts of multi-modal images. We have been exploring ways by investigating local image features.Two main contributions have been made in this thesis.First, we have improved existing mono-modal and multi-modal image registration techniques by better utilizing gradient information. For a feature-based image registration technique, its effectiveness to a large extent relies on the discrimination power of local descriptors. In the existing techniques, gradient information is utilized in a number of ways for building local descriptors. We have analyzed the limitations of these techniques, and have proposed a technique for better utilizing gradient information. As a result, the discrimination power of local descriptors has been enhanced, leading to a better registration performance.Second, we have developed a new multi-modal image registration technique, which has the following innovations:1. We have proposed a technique to detect the intrinsic structural similarity in multi-modal microscopic images. This is achieved by exploiting the characteristics in intensity relationships between the Red-Green-Blue color channels.2. To increase robustness to content differences, contour-based corners are used, instead of intensity-based keypoints in a state-of-the-art multi-modal image registration technique.3. We have proposed a new local descriptor to better represent corners.4. We have proposed a new way of scale estimation by making use of geometric relationships between corner triplets in two images.The proposed multi-modal image registration technique achieves greater robustness in terms of both content differences and scale differences as compared to the state-of-the-art multi-modal image registration technique.

Download or read book Medical Image Computing and Computer Assisted Intervention MICCAI 2015 written by Nassir Navab and published by Springer. This book was released on 2015-09-28 with total page 739 pages. Available in PDF, EPUB and Kindle. Book excerpt: The three-volume set LNCS 9349, 9350, and 9351 constitutes the refereed proceedings of the 18th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2015, held in Munich, Germany, in October 2015. Based on rigorous peer reviews, the program committee carefully selected 263 revised papers from 810 submissions for presentation in three volumes. The papers have been organized in the following topical sections: quantitative image analysis I: segmentation and measurement; computer-aided diagnosis: machine learning; computer-aided diagnosis: automation; quantitative image analysis II: classification, detection, features, and morphology; advanced MRI: diffusion, fMRI, DCE; quantitative image analysis III: motion, deformation, development and degeneration; quantitative image analysis IV: microscopy, fluorescence and histological imagery; registration: method and advanced applications; reconstruction, image formation, advanced acquisition - computational imaging; modelling and simulation for diagnosis and interventional planning; computer-assisted and image-guided interventions.

Download or read book Medical Image Computing and Computer Assisted Intervention MICCAI 2013 written by Kensaku Mori and published by Springer. This book was released on 2013-09-20 with total page 836 pages. Available in PDF, EPUB and Kindle. Book excerpt: The three-volume set LNCS 8149, 8150, and 8151 constitutes the refereed proceedings of the 16th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2013, held in Nagoya, Japan, in September 2013. Based on rigorous peer reviews, the program committee carefully selected 262 revised papers from 789 submissions for presentation in three volumes. The 95 papers included in the first volume have been organized in the following topical sections: physiological modeling and computer-assisted intervention; imaging, reconstruction, and enhancement; registration; machine learning, statistical modeling, and atlases; computer-aided diagnosis and imaging biomarkers; intraoperative guidance and robotics; microscope, optical imaging, and histology; cardiology, vasculatures and tubular structures; brain imaging and basic techniques; diffusion MRI; and brain segmentation and atlases.

Download or read book An Effective Technique for Multi modal Image Registration written by Md Tanvir Hossain and published by . This book was released on 2012 with total page 284 pages. Available in PDF, EPUB and Kindle. Book excerpt: Image registration brings two images into alignment despite any initial misalignment. Multi-modal image registration is a special type of registration problem where complexity is added, due to images being captured by different types of imaging devices. For decades, researchers have continued to work on multi-modal image registration. Yet limited attention has been paid to developing local feature descriptors for multi-modal registration, despite their useful performance in general registration problems. We would argue that multi-modal registration can therefore benefit from qualities that are inherent in local techniques.In this thesis, we focus on developing a new multi-modal image registration technique based on local descriptors. We study a range of local feature description techniques, acquire an in-depth understanding of relevant sub-procedures and propose a novel modality invariant feature description technique that can be effectively applied to multi-modal image registration. The proposed technique has four components which are carefully designed to cater for variations in image properties caused by multi-modality as well as to enable finding correspondences with high accuracy under a wide range of transformations.We identify certain properties of multi-modal images, namely gradient and region reversal, which must be addressed appropriately in order to register multi-modal images. The first component of our proposed technique overcomes problems with existing local multi-modal techniques in addressing these properties. The second component comprises strategies to encode gradient profiles that are appropriate for multi-modal registration applications. The third component employs a robust and powerful data fitting technique, namely Hough Transform, in order to achieve high accuracy in multi-modal registration. Our proposed solution is simple, but effective, and enables seamless integration of Hough Transform by overcoming existing technical limitations. The final component incorporates special methodologies to cater for local rotations in input images. Besides evaluating our technique in terms of the accuracy of identified correspondences, we also show that the proposed technique results in less registration error when compared with existing techniques.

Download or read book Fuzzy Systems and Data Mining V written by A.J. Tallón-Ballesteros and published by IOS Press. This book was released on 2019-11-06 with total page 1186 pages. Available in PDF, EPUB and Kindle. Book excerpt: The Fuzzy Systems and Data Mining (FSDM) conference is an annual event encompassing four main themes: fuzzy theory, algorithms and systems, which includes topics like stability, foundations and control; fuzzy application, which covers different kinds of processing as well as hardware and architectures for big data and time series and has wide applicability; the interdisciplinary field of fuzzy logic and data mining, encompassing applications in electrical, industrial, chemical and engineering fields as well as management and environmental issues; and data mining, outlining new approaches to big data, massive data, scalable, parallel and distributed algorithms. The annual conference provides a platform for knowledge exchange between international experts, researchers, academics and delegates from industry. This book includes the papers accepted and presented at the 5th International Conference on Fuzzy Systems and Data Mining (FSDM 2019), held in Kitakyushu, Japan on 18-21 October 2019. This year, FSDM received 442 submissions. All papers were carefully reviewed by program committee members, taking account of the quality, novelty, soundness, breadth and depth of the research topics falling within the scope of FSDM. The committee finally decided to accept 137 papers, which represents an acceptance rate of about 30%. The papers presented here are arranged in two sections: Fuzzy Sets and Data Mining, and Communications and Networks. Providing an overview of the most recent scientific and technological advances in the fields of fuzzy systems and data mining, the book will be of interest to all those working in these fields.

Download or read book Multi modal Image Registration with Unsupervised Deep Learning written by Courtney K. Guo and published by . This book was released on 2019 with total page 40 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this thesis, we tackle learning-based multi-modal image registration. Multi-modal registration, in which two images of dierent modalities need to be aligned to each other, is a difficult yet essential task for medical imaging analysis. Classical methods have been developed for single-modal and multi-modal registration, but are slow because they solve an optimization problem for each pair of images. Recently, deep learning methods for registration have been proposed, and have been shown to shorten registration time by learning a global function to perform registration, which can then be applied quickly on a pair of test images. These methods perform well for single-modal registration but have not yet been extended to the harder task of multi-modal registration. We bridge this gap by implementing classical multi-modal metrics in a differentiable and efficient manner to enable deep image registration for multi-modal data. We nd that our method for multi-modal registration performs significantly better than baselines, in terms of both accuracy and runtime.

Download or read book Medical Image Registration written by Joseph V. Hajnal and published by CRC Press. This book was released on 2001-06-27 with total page 394 pages. Available in PDF, EPUB and Kindle. Book excerpt: Image registration is the process of systematically placing separate images in a common frame of reference so that the information they contain can be optimally integrated or compared. This is becoming the central tool for image analysis, understanding, and visualization in both medical and scientific applications. Medical Image Registration provid

Download or read book Image Registration for Remote Sensing written by Jacqueline Le Moigne and published by Cambridge University Press. This book was released on 2011-03-24 with total page 515 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides a summary of current research in the application of image registration to satellite imagery. Presenting algorithms for creating mosaics and tracking changes on the planet's surface over time, it is an indispensable resource for researchers and advanced students in Earth and space science, and image processing.

Download or read book Quantification of Brain Function Using PET written by and published by Elsevier. This book was released on 1996-07-17 with total page 481 pages. Available in PDF, EPUB and Kindle. Book excerpt: Functional imaging of the brain is one of the most rapidly advancing areas of neuroscience and Positron Emission Tomography (PET) plays a major role in this progress. This book provides a comprehensive overview of the current status of PET and state-of-the-art neuroimaging. It is comprised of summaries of the presentations by experts in the field. Topics covered include radiotracer selection, advances in instrumentation, image reconstruction and data analysis, and statistical mapping of brain activity. This book focuses on the accuracy of the functional image and the strategies for addressing clinical, scientific, and diagnostic questions.Covers the PET imaging process from tracer selection to analysis and interpretationContains 79 concise reports with abundant illustrationsThe definitive state-of-the-art book for functional neuroscience with PET

Download or read book Biomedical Image Registration written by Žiga Špiclin and published by Springer Nature. This book was released on 2020-06-09 with total page 178 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 9th International Workshop on Biomedical Image Registration, WBIR 2020, which was supposed to be held in Portorož, Slovenia, in June 2020. The conference was postponed until December 2020 due to the COVID-19 pandemic. The 16 full and poster papers included in this volume were carefully reviewed and selected from 22 submitted papers. The papers are organized in the following topical sections: Registration initialization and acceleration, interventional registration, landmark based registration, multi-channel registration, and sliding motion.

Download or read book Computer Vision Metrics written by Scott Krig and published by Apress. This book was released on 2014-06-14 with total page 498 pages. Available in PDF, EPUB and Kindle. Book excerpt: Computer Vision Metrics provides an extensive survey and analysis of over 100 current and historical feature description and machine vision methods, with a detailed taxonomy for local, regional and global features. This book provides necessary background to develop intuition about why interest point detectors and feature descriptors actually work, how they are designed, with observations about tuning the methods for achieving robustness and invariance targets for specific applications. The survey is broader than it is deep, with over 540 references provided to dig deeper. The taxonomy includes search methods, spectra components, descriptor representation, shape, distance functions, accuracy, efficiency, robustness and invariance attributes, and more. Rather than providing ‘how-to’ source code examples and shortcuts, this book provides a counterpoint discussion to the many fine opencv community source code resources available for hands-on practitioners.

Download or read book Multimodal Scene Understanding written by Michael Ying Yang and published by Academic Press. This book was released on 2019-07-16 with total page 424 pages. Available in PDF, EPUB and Kindle. Book excerpt: Multimodal Scene Understanding: Algorithms, Applications and Deep Learning presents recent advances in multi-modal computing, with a focus on computer vision and photogrammetry. It provides the latest algorithms and applications that involve combining multiple sources of information and describes the role and approaches of multi-sensory data and multi-modal deep learning. The book is ideal for researchers from the fields of computer vision, remote sensing, robotics, and photogrammetry, thus helping foster interdisciplinary interaction and collaboration between these realms. Researchers collecting and analyzing multi-sensory data collections – for example, KITTI benchmark (stereo+laser) - from different platforms, such as autonomous vehicles, surveillance cameras, UAVs, planes and satellites will find this book to be very useful. - Contains state-of-the-art developments on multi-modal computing - Shines a focus on algorithms and applications - Presents novel deep learning topics on multi-sensor fusion and multi-modal deep learning

Download or read book Representations and Techniques for 3D Object Recognition and Scene Interpretation written by Derek Hoiem and published by Morgan & Claypool Publishers. This book was released on 2011 with total page 172 pages. Available in PDF, EPUB and Kindle. Book excerpt: One of the grand challenges of artificial intelligence is to enable computers to interpret 3D scenes and objects from imagery. This book organizes and introduces major concepts in 3D scene and object representation and inference from still images, with a focus on recent efforts to fuse models of geometry and perspective with statistical machine learning. The book is organized into three sections: (1) Interpretation of Physical Space; (2) Recognition of 3D Objects; and (3) Integrated 3D Scene Interpretation. The first discusses representations of spatial layout and techniques to interpret physical scenes from images. The second section introduces representations for 3D object categories that account for the intrinsically 3D nature of objects and provide robustness to change in viewpoints. The third section discusses strategies to unite inference of scene geometry and object pose and identity into a coherent scene interpretation. Each section broadly surveys important ideas from cognitive science and artificial intelligence research, organizes and discusses key concepts and techniques from recent work in computer vision, and describes a few sample approaches in detail. Newcomers to computer vision will benefit from introductions to basic concepts, such as single-view geometry and image classification, while experts and novices alike may find inspiration from the book's organization and discussion of the most recent ideas in 3D scene understanding and 3D object recognition. Specific topics include: mathematics of perspective geometry; visual elements of the physical scene, structural 3D scene representations; techniques and features for image and region categorization; historical perspective, computational models, and datasets and machine learning techniques for 3D object recognition; inferences of geometrical attributes of objects, such as size and pose; and probabilistic and feature-passing approaches for contextual reasoning about 3D objects and scenes. Table of Contents: Background on 3D Scene Models / Single-view Geometry / Modeling the Physical Scene / Categorizing Images and Regions / Examples of 3D Scene Interpretation / Background on 3D Recognition / Modeling 3D Objects / Recognizing and Understanding 3D Objects / Examples of 2D 1/2 Layout Models / Reasoning about Objects and Scenes / Cascades of Classifiers / Conclusion and Future Directions

Download or read book Artificial Intelligence Abstracts written by and published by . This book was released on 1988 with total page 474 pages. Available in PDF, EPUB and Kindle. Book excerpt: