Download or read book Automated Brain Lesion Segmentation in Magnetic Resonance Images written by Simon Andermatt and published by . This book was released on 2019 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Automated Brain Lesion Detection and Segmentation Using Magnetic Resonance Images written by Nooshin Nabizadeh and published by . This book was released on 2015 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Automated segmentation of brain lesions in magnetic resonance images (MRI) is a difficult procedure due to the variability and complexity of the location, size, shape, and texture of these lesions. In this study, four algorithms for brain lesion detection and segmentation using MRI are proposed. In the first algorithm, an automatic algorithm for brain stroke lesion detection and segmentation using single-spectral MRI is proposed, which is called histogram-based gravitational optimization algorithm (HGOA). HGOA is a novel intensity-based segmentation technique that applies enhanced gravitational optimization algorithm on histogram analysis results to segment the brain lesion. The ischemic stroke lesions are segmented with 91.5% accuracy and tumor lesions are segmented with 88% accuracy. Since histogram analysis limits the extracted information to the number of pixels in specific gray levels and does not include any region-based information, the accuracy of a histogram-based method is limited. In the second algorithm, in order to increase the accuracy of brain tumor segmentation, a texture-based automated approach is presented. The experimental results on T1-weighted, T2-weighted, and fluid-attenuated inversion recovery (FLAIR) images on both simulated and real brain MRI data prove the efficacy of our technique in successfully segmentation of brain tumor tissues with high accuracy (95.9 ± 0.4% for database of simulated MR images, and 93.2 ± 0.3% for database of real MR images). In order to reduce the computational complexity and expedite the segmentation algorithm, and also to improve the system performance, some modifications are applied in the algorithm presented in previous algorithm. In the third algorithm, a fully automatic tumor system, which is combination of texture-based and contour-based algorithms is presented. Skippy greedy snake algorithm is capable of segmenting the tumor area; however, the algorithm's accuracy and performance depends significantly on its initial points. Here, we modify the previous algorithm to automatically find proper initial points, which not only obviates the requirement of manual interference, but also increase the accuracy and speed of optimization convergence. Comparing with previous method, this method achieves higher accuracy in tumor segmentation (96.8 ± 0.3% for database of simulated MR images, and 93.8 ± 0.1% for database of real MR images) and lower computational complexity. The intensity similarities between brain lesions and some normal tissues result in confusion within segmentation algorithms, especially in the database of real MR images. In order to improve the system performance for this database, a multi-spectral approach based on feature-level fusion is presented in forth algorithm. Even though using multi-spectral MRI has several drawbacks and limitations, since it makes use of complementary information, it increases the accuracy of the system. Here, a feature-level fusion technique based on canonical correlation analysis (CCA) is proposed. It is worth mentioning that for the first time CCA is applied for combining MRI sequences in order to segment tumors. Even though data fusion increases computational complexity of the segmentation algorithm, it results in a higher accuracy (95.8 ± 0.2% for database of real MR images).
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
Download or read book Automated Brain Lesion Detection and Segmentation Using MR Images written by Nabizadeh Nooshin and published by LAP Lambert Academic Publishing. This book was released on 2015-07-27 with total page 272 pages. Available in PDF, EPUB and Kindle. Book excerpt: Computer vision and machine learning allows the image data to be seen by a computer or machine as a person would see it. This is a complex concept for a computer to comprehend since computers do not understand the three-dimensional perspective as a person views and understands it. Computer vision has variety of applications in industry, medicine, surveillance systems, video analysis, robotic, and etc. Image segmentation is one of the most challenging topics in computer vision and machine learning. As an application of image segmentation in biomedical research is to localize some specific cells and tissues, e.g., tumor or stroke in magnetic resonance images (MRI). Medical image segmentation helps physicians to find these lesions more accurately, and it can be great source of information in emergency cases that specialist is not available. Therefore, it is an important process in computerized medical imaging. Automated segmentation of brain lesions in MRI is a difficult procedure due to the variability and complexity of the location, size, shape, and texture of these lesions. This study presents four algorithms for brain lesion detection and segmentation using MR images.
Download or read book Medical Imaging and Augmented Reality written by Guang-Zhong Yang and published by Springer Science & Business Media. This book was released on 2004-08-11 with total page 389 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Brain Mri Segmentation Using Texture Features written by Anuradha Phadke and published by LAP Lambert Academic Publishing. This book was released on 2012-08 with total page 88 pages. Available in PDF, EPUB and Kindle. Book excerpt: The main aim of this book is to introduce to a system which can detect brain tumor using brain Magnetic Resonance Image segmentation. Automated MRI (Magnetic Resonance Imaging) brain tumor segmentation is a difficult task due to the variance and complexity of tumors. In this work, a statistical structure analysis based brain tissue segmentation scheme is presented, which focuses on the structural analysis on both abnormal and normal tissues. As the local textures in the images can reveal the typical 'regularities' of biological structures, textural features have been extracted using co-occurrence matrix approach. By the analysis of level of correlation the number of features can be reduced to the significant components. Feed forward back propagation neural network is used for classification. Proposed techniques of analysis and classification are used to investigate the differences of texture features among macroscopic lesion white matter (LWM) and normal appearing white matter (NAWM) in magnetic resonance images (MRI) from patients with normal and abnormal white matter.
Download or read book Automated Brain Structure Segmentation in Magnetic Resonance Images of Multiple Sclerosis Patients written by Sandra González-Villà and published by . This book was released on 2019 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This thesis is focused on the automated segmentation of the brain structures in magnetic resonance images, applied to multiple sclerosis patients. This disease is characterized by the presence of lesions, which affect the segmentation result of commonly used automatic methods. We propose a new correspondence search model able to minimize this problem and extend the theory of two remarkable label fusion strategies of the literature, i.e. Non-local Spatial STAPLE and Joint Label Fusion, in order to integrate this model into their corresponding estimation algorithms. Furthermore, with the aim of providing fully automated algorithms, a whole automated pipeline is presented. Finally, a second extension of the theory to enable the integration of manual and automatic edits into the segmentation estimation of both strategies is also proposed. The analysis of the results obtained points out a performance improvement on the lesion areas, which is also reflected on the whole brain segmentation performance.
Download or read book Brainlesion Glioma Multiple Sclerosis Stroke and Traumatic Brain Injuries written by Alessandro Crimi and published by Springer. This book was released on 2018-02-16 with total page 524 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes revised selected papers from the Third International MICCAI Brainlesion Workshop, BrainLes 2017, as well as the International Multimodal Brain Tumor Segmentation, BraTS, and White Matter Hyperintensities, WMH, segmentation challenges, which were held jointly at the Medical Image computing for Computer Assisted Intervention Conference, MICCAI, in Quebec City, Canada, in September 2017. The 40 papers presented in this volume were carefully reviewed and selected from 46 submissions. They were organized in topical sections named: brain lesion image analysis; brain tumor image segmentation; and ischemic stroke lesion image segmentation.
Download or read book Brainlesion Glioma Multiple Sclerosis Stroke and Traumatic Brain Injuries written by Alessandro Crimi and published by Springer. This book was released on 2016-03-18 with total page 303 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the thoroughly refereed post-workshop proceedings of the International Workshop on Brain Lesion (BrainLes), Brain Tumor Segmentation (BRATS) and Ischemic Stroke Lesion Segmentation (ISLES), held in Munich, Germany, on October 5, 2015, in conjunction with the International Conference on Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2015. The 25 papers presented in this volume were carefully reviewed and selected from 28 submissions. They are grouped around the following topics: brain lesion image analysis; brain tumor image segmentation; ischemic stroke lesion image segmentation.
Download or read book Automatic Segmentation of White Matter Lesions from MRI Data written by Shenshen Shenshen and published by LAP Lambert Academic Publishing. This book was released on 2010-06 with total page 64 pages. Available in PDF, EPUB and Kindle. Book excerpt: A fully automatic white matter lesion segmentation method has been developed and evaluated. The method uses multispectral magnetic resonance imaging (MRI) data (T1, T2 and Proton Density). First fuzzy c means (FCM) was used to segment normal brain tissues (white matter, grey matter, and cerebrospinal fluid). The holes in normal white matter were used to sample the WML intensities in the different images. The segmentation of WML was optimized by a graph cut approach. The method was trained by using 9 manually segmented datasets and evaluated by comparison to 11 other manually segmented, and visually evaluated, datasets. The graph cut part of the automatic segmentation requires, on average, 30 seconds per dataset. The results correlated well (r=0.954) to a manually created reference that was supervised by two neuroradiologists.
Download or read book MRI Atlas of MS Lesions written by M.A. Sahraian and published by Springer Science & Business Media. This book was released on 2007-10-16 with total page 184 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
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 801 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 Contributions to the Automated Segmentation of Brain Tumors in Magnetic Resonance Images written by Michael Kaus and published by . This book was released on 2000 with total page 150 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Automatic Brain Tumor Segmentation with Convolutional Neural Network written by Meet Shah and published by . This book was released on 2020 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: There are multiple types of Brain Tumors, which can be difficult to evaluate that leads to unpleasant result for the patient. Thus, detection and treatment planning of the brain tumor is the most important factor in the process. Magnetic resonance imaging (MRI) is broadly used technique to evaluate the brain tumors. Manual segmentation of brain tumor from MRI consumes more time and depended on the experience of the machinist. Thus, automated techniques for the segmentation are required to ease the treatment planning. Even in the automated methods for the segmentation is not so easy because of the various types of the brain tumors. Thus, it is necessary to have reliable method for brain tumor segmentation which can measure the tumors efficiently and less time consuming. In this paper, we propose a technique for brain tumor segmentation which is created using U-Net based convolutional neural network. The technique was evaluated on datasets called Multimodal Brain Tumor Image Segmentation (BRATS 2019). This dataset contains more than 76 cases of low-grade tumor and 259 cases of high-grade tumor.
Download or read book Probabilistic Modeling for Segmentation in Magnetic Resonance Images of the Human Brain written by Michael Wels and published by Logos Verlag Berlin GmbH. This book was released on 2010 with total page 147 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this book the fully automatic generation of semantic annotations for medical imaging data by means of medical image segmentation and labeling is addressed. In particular, the focus is on the segmentation of the human brain and related structures from magnetic resonance imaging (MRI) data. Three novel probabilistic methods from the field of database-guided knowledge-based medical image segmentation are presented. Each of the methods is applied to one of three MRI segmentation scenarios: 1) 3-D MRI brain tissue classification and intensity non-uniformity correction, 2) pediatric brain cancer segmentation in multi-spectral 3-D MRI, and 3) 3-D MRI anatomical brain structure segmentation. All the newly developed methods make use of domain knowledge encoded by probabilistic boosting-trees (PBT), which is a recent machine learning technique. For all the methods uniform probabilistic formalisms are presented that group the methods into the broader context of probabilistic modeling for the purpose of image segmentation. It is shown by comparison with other methods from the literature that in all the scenarios the newly developed algorithms in most cases give more accurate results and have a lower computational cost. Evaluation on publicly available benchmarking data sets ensures reliable comparability of the results to those of other current and future methods. One of the methods successfully participated in the ongoing online caudate segmentation challenge (www.cause07.org), where it ranks among the top five methods for this particular segmentation scenario.
Download or read book Brain Lesion Detection and Tumor Segmentation in MRI Using 3D Fully Convolutional Networks written by Andrew Jesson and published by . This book was released on 2019 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: "This thesis presents a generalized framework for the detection of lesions and segmentation of tumors in brain magnetic resonance imaging (MRI) using fully convolutional neural networks (FCNs). The FCN framework is chosen due to its capacity to model multi-resolution context in the image domain and yield consistent semantic segmentation results. This thesis extends the FCN framework to better suit the task of brain lesion segmentation and detection by including 3D convolutions to capture the full context of MRI volumes, a curriculum on the label weights to handle class imbalance, and a multi-scale loss to promote the modelling of context in the label domain. The proposed method is evaluated on two distinct tasks: multiple sclerosis (MS) lesion detection, and brain tumor segmentation. It is shown that this method performs at a high level for both tasks even though no fundamental changes to architecture, objective function, or optimization strategy are made. For the task of MS lesion detection, the trained model achieves a true positive rate of 0.82 at a false detection rate of 0.23 on an independent test set. The method was also submitted to the 2017 MICCAI Brain Tumor Segmentation (BraTS) Challenge, where it placed in the top five out of nearly one-hundred entrants, achieving independently evaluated dices scores of 0.860 and 0.783 for segmenting tumor and tumor core on unseen test data." --
Download or read book Deep Learning and Data Labeling for Medical Applications written by Gustavo Carneiro and published by Springer. This book was released on 2016-10-07 with total page 289 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
