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Author: Bassem A Abdullah Publisher: ISBN: Category : Languages : en Pages :
Book Description
Multiple Sclerosis (MS) is an autoimmune disease of central nervous system. It may result in a variety of symptoms from blurred vision to severe muscle weakness and degradation, depending on the affected regions in brain. To better understand this disease and to quantify its evolution, magnetic resonance imaging (MRI) is increasingly used nowadays. Manual delineation of MS lesions in MR images by human expert is time-consuming, subjective, and prone to inter-expert variability. Therefore, automatic segmentation is needed as an alternative to manual segmentation. However, the progression of the MS lesions shows considerable variability and MS lesions present temporal changes in shape, location, and area between patients and even for the same patient, which renders the automatic segmentation of MS lesions a challenging problem. In this dissertation, a set of segmentation pipelines are proposed for automatic segmentation of multiple sclerosis (MS) lesions from brain magnetic resonance imaging (MRI) data. These techniques use a trained support vector machine (SVM) to discriminate between the blocks in regions of MS lesions and the blocks in non-MS lesion regions mainly based on the textural features with aid of the other features. The main contribution of this set of frameworks is the use of textural features to detect MS lesions in a fully automated approach that does not rely on manually delineating the MS lesions. In addition, the technique introduces the concept of the multi-sectional views segmentation to produce verified segmentation. The multi-sectional views pipeline is customized to provide better segmentation performance and to benefit from the properties and the nature of MS lesion in MRI. These customization and enhancement leads to development of the customized MV-T-SVM. The MRI datasets that were used in the evaluation of the proposed pipelines are simulated MRI datasets (3 subjects) generated using the McGill University BrainWeb MRI Simulator, real datasets (51 subjects) publicly available at the workshop of MS Lesion Segmentation Challenge 2008 and real MRI datasets (10 subjects) for MS subjects acquired at the University of Miami. The obtained results indicate that the proposed method would be viable for use in clinical practice for the detection of MS lesions in MRI.
Author: Bassem A Abdullah Publisher: ISBN: Category : Languages : en Pages :
Book Description
Multiple Sclerosis (MS) is an autoimmune disease of central nervous system. It may result in a variety of symptoms from blurred vision to severe muscle weakness and degradation, depending on the affected regions in brain. To better understand this disease and to quantify its evolution, magnetic resonance imaging (MRI) is increasingly used nowadays. Manual delineation of MS lesions in MR images by human expert is time-consuming, subjective, and prone to inter-expert variability. Therefore, automatic segmentation is needed as an alternative to manual segmentation. However, the progression of the MS lesions shows considerable variability and MS lesions present temporal changes in shape, location, and area between patients and even for the same patient, which renders the automatic segmentation of MS lesions a challenging problem. In this dissertation, a set of segmentation pipelines are proposed for automatic segmentation of multiple sclerosis (MS) lesions from brain magnetic resonance imaging (MRI) data. These techniques use a trained support vector machine (SVM) to discriminate between the blocks in regions of MS lesions and the blocks in non-MS lesion regions mainly based on the textural features with aid of the other features. The main contribution of this set of frameworks is the use of textural features to detect MS lesions in a fully automated approach that does not rely on manually delineating the MS lesions. In addition, the technique introduces the concept of the multi-sectional views segmentation to produce verified segmentation. The multi-sectional views pipeline is customized to provide better segmentation performance and to benefit from the properties and the nature of MS lesion in MRI. These customization and enhancement leads to development of the customized MV-T-SVM. The MRI datasets that were used in the evaluation of the proposed pipelines are simulated MRI datasets (3 subjects) generated using the McGill University BrainWeb MRI Simulator, real datasets (51 subjects) publicly available at the workshop of MS Lesion Segmentation Challenge 2008 and real MRI datasets (10 subjects) for MS subjects acquired at the University of Miami. The obtained results indicate that the proposed method would be viable for use in clinical practice for the detection of MS lesions in MRI.
Author: Guang-Zhong Yang Publisher: Springer Science & Business Media ISBN: 3540228772 Category : Medical Languages : en Pages : 389
Book Description
Rapid technical advances in medical imaging, including its growing application to drug/gene therapy and invasive/interventional procedures, have attracted significant interest in close integration of research in life sciences, medicine, physical sciences and engineering. This is motivated by the clinical and basic science research requi- ment of obtaining more detailed physiological and pathological information about the body for establishing localized genesis and progression of diseases. Current research is also motivated by the fact that medical imaging is increasingly moving from a primarily diagnostic modality towards a therapeutic and interventional aid, driven by recent advances in minimal-access and robotic-assisted surgery. It was our great pleasure to welcome the attendees to MIAR 2004, the 2nd Int- national Workshop on Medical Imaging and Augmented Reality, held at the Xia- shan (Fragrant Hills) Hotel, Beijing, during August 19–20, 2004. The goal of MIAR 2004 was to bring together researchers in computer vision, graphics, robotics, and medical imaging to present the state-of-the-art developments in this ever-growing research area. The meeting consisted of a single track of oral/poster presentations, with each session led by an invited lecture from our distinguished international f- ulty members. For MIAR 2004, we received 93 full submissions, which were sub- quently reviewed by up to 5 reviewers, resulting in the acceptance of the 41 full - pers included in this volume.
Author: Nassir Navab Publisher: Springer ISBN: 3319245740 Category : Computers Languages : en Pages : 801
Book Description
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.
Author: M.A. Sahraian Publisher: Springer Science & Business Media ISBN: 3540713719 Category : Medical Languages : en Pages : 184
Book Description
MRI has become the main paraclinical test in the diagnosis and management of multiple sclerosis. We have demonstrated more than 400 pictures of different typical and atypical MS lesions in this atlas. Each image has a teaching point. New diagnostic criteria and differential diagnosis have been discussed.
Author: Gustavo Carneiro Publisher: Springer ISBN: 3319469762 Category : Computers Languages : en Pages : 289
Book Description
This book constitutes the refereed proceedings of two workshops held at the 19th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2016, in Athens, Greece, in October 2016: the First Workshop on Large-Scale Annotation of Biomedical Data and Expert Label Synthesis, LABELS 2016, and the Second International Workshop on Deep Learning in Medical Image Analysis, DLMIA 2016. The 28 revised regular papers presented in this book were carefully reviewed and selected from a total of 52 submissions. The 7 papers selected for LABELS deal with topics from the following fields: crowd-sourcing methods; active learning; transfer learning; semi-supervised learning; and modeling of label uncertainty.The 21 papers selected for DLMIA span a wide range of topics such as image description; medical imaging-based diagnosis; medical signal-based diagnosis; medical image reconstruction and model selection using deep learning techniques; meta-heuristic techniques for fine-tuning parameter in deep learning-based architectures; and applications based on deep learning techniques.
Author: Daniel Biediger Publisher: ISBN: Category : Computer science Languages : en Pages :
Book Description
Multiple sclerosis (MS) is an autoimmune disease of the central nervous system that causes damage to the insulating myelin sheaths around the axons in the brain. It affects over 2.5 million people world-wide. The disease progresses at different rates in different people and can have periods of remission and relapse. A fast and accurate method for evaluating the number and size of MS lesions in the brain is a key component in evaluating the progress of the disease and the efficacy of treatments. MS lesion segmentation usually requires the expertise of a trained physician. Manual segmentation is slow and difficult and the results can be somewhat subjective. While many automated methods exist, they do not provide sufficiently accurate segmentation results. There exists a need for a robust, fast, and accurate method for automatically segmenting MS lesions. This thesis presents the results of an effort to improve the segmentation results of an existing system for lesion segmentation in MRI images. It includes two different strategies to improve the segmentation results by addressing opportunities missed in the existing approach. The first strategy leverages the current processing system at a granularity finer than the whole-brain to detect lesions at a local level. The existing system makes global estimates on the tissue intensities. Because these intensities vary across the brain, the global assumption provides inaccurate estimates in some cases. The first improvement combines a series of local results to produce a whole-brain lesion segmentation. This approach better captures the local lesion properties and produces encouraging results, with a general improvement in the detection rate of lesions. The second method looks at the individual voxel level and the local intensity neighborhood. As a post-processing method, it selects seed points from the results of the previous step. It uses a region growing method based on cellular automata to expand the lesion areas based on a local neighborhood similarity in intensity. While it provides some benefit, it is sensitive to initial conditions and the results depend on the implementation details.
Author: Marc Niethammer Publisher: Springer ISBN: 3319590502 Category : Computers Languages : en Pages : 691
Book Description
This book constitutes the proceedings of the 25th International Conference on Information Processing in Medical Imaging, IPMI 2017, held at the Appalachian State University, Boon, NC, USA, in June 2017. The 53 full papers presented in this volume were carefully reviewed and selected from 147 submissions. They were organized in topical sections named: analysis on manifolds; shape analysis; disease diagnosis/progression; brain networks an connectivity; diffusion imaging; quantitative imaging; imaging genomics; image registration; segmentation; general image analysis.
Author: Bassem A Abdullah
Author: Bassem A Abdullah
Author: Guang-Zhong Yang
Author: Nassir Navab
Author: 劉彥良
Author: M.A. Sahraian
Author: Gustavo Carneiro
Author: Jürg Kesselring
Author: Daniel Biediger
Author: Marc Niethammer
Author: Olivier Commowick