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 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 Texture Analysis for Magnetic Resonance Imaging written by Milan Hájek and published by Texture Analysis Magn Resona. This book was released on 2006 with total page 248 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Handbook of Research on Disease Prediction Through Data Analytics and Machine Learning written by Rani, Geeta and published by IGI Global. This book was released on 2020-10-16 with total page 586 pages. Available in PDF, EPUB and Kindle. Book excerpt: By applying data analytics techniques and machine learning algorithms to predict disease, medical practitioners can more accurately diagnose and treat patients. However, researchers face problems in identifying suitable algorithms for pre-processing, transformations, and the integration of clinical data in a single module, as well as seeking different ways to build and evaluate models. The Handbook of Research on Disease Prediction Through Data Analytics and Machine Learning is a pivotal reference source that explores the application of algorithms to making disease predictions through the identification of symptoms and information retrieval from images such as MRIs, ECGs, EEGs, etc. Highlighting a wide range of topics including clinical decision support systems, biomedical image analysis, and prediction models, this book is ideally designed for clinicians, physicians, programmers, computer engineers, IT specialists, data analysts, hospital administrators, researchers, academicians, and graduate and post-graduate students.

Download or read book Handbook of Texture Analysis written by Ayman El-Baz and published by CRC Press. This book was released on 2024-06-24 with total page 226 pages. Available in PDF, EPUB and Kindle. Book excerpt: The major goals of texture research in computer vision are to understand, model, and process texture, and ultimately, to simulate the human visual learning process using computer technologies. In the last decade, artificial intelligence has been revolutionized by machine learning and big data approaches, outperforming human prediction on a wide range of problems. In particular, deep learning convolutional neural networks (CNNs) are particularly well suited to texture analysis. This book examines four major application domains related to texture analysis and their relationship to AI-based industrial applications: texture classification, texture segmentation, shape from texture, and texture synthesis. This volume: Discusses texture-based segmentation for extracting image shape features, modeling and segmentation of noisy and textured images, spatially constrained color-texture model for image segmentation, and texture segmentation using Gabor filters Examines textural features for image classification, a statistical approach for classification, texture classification from random features, and applications of texture classifications Describes shape from texture, including general principles, 3D shapes, and equations for recovering shape from texture Surveys texture modeling, including extraction based on Hough transformation and cycle detection, image quilting, gray level run lengths, and use of Markov random fields Aimed at researchers, academics, and advanced students in biomedical engineering, image analysis, cognitive science, and computer science and engineering, this is an essential reference for those looking to advance their understanding in this applied and emergent field.

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 2017-04-11 with total page 297 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, as well as the challenges on Brain Tumor Segmentation (BRATS), Ischemic Stroke Lesion Image Segmentation (ISLES), and the Mild Traumatic Brain Injury Outcome Prediction (mTOP), held in Athens, October 17, 2016, in conjunction with the International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2016. The 26 papers presented in this volume were carefully reviewed. They present the latest advances in segmentation, disease prognosis and other applications to the clinical context.

Download or read book Computational Intelligence and Information Technology written by Vinu Das and published by Springer Science & Business Media. This book was released on 2013-01-02 with total page 900 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the proceedings of the First International Conference on Computational Intelligence and Information Technology, CIIT 2011, held in Pune, India, in November 2011. The 58 revised full papers, 67 revised short papers, and 32 poster papers presented were carefully reviewed and selected from 483 initial submissions. The papers are contributed by innovative academics and industrial experts in the field of computer science, information technology, computational engineering, mobile communication and security and offer a stage to a common forum, where a constructive dialog on theoretical concepts, practical ideas and results of the state of the art can be developed.

Download or read book Handbook of Pattern Recognition and Computer Vision written by C. H. Chen and published by World Scientific. This book was released on 1999 with total page 1045 pages. Available in PDF, EPUB and Kindle. Book excerpt: The very significant advances in computer vision and pattern recognition and their applications in the last few years reflect the strong and growing interest in the field as well as the many opportunities and challenges it offers. The second edition of this handbook represents both the latest progress and updated knowledge in this dynamic field. The applications and technological issues are particularly emphasized in this edition to reflect the wide applicability of the field in many practical problems. To keep the book in a single volume, it is not possible to retain all chapters of the first edition. However, the chapters of both editions are well written for permanent reference.

Download or read book Advances in Computing and Information Technology written by Natarajan Meghanathan and published by Springer Science & Business Media. This book was released on 2012-08-11 with total page 901 pages. Available in PDF, EPUB and Kindle. Book excerpt: The international conference on Advances in Computing and Information technology (ACITY 2012) provides an excellent international forum for both academics and professionals for sharing knowledge and results in theory, methodology and applications of Computer Science and Information Technology. The Second International Conference on Advances in Computing and Information technology (ACITY 2012), held in Chennai, India, during July 13-15, 2012, covered a number of topics in all major fields of Computer Science and Information Technology including: networking and communications, network security and applications, web and internet computing, ubiquitous computing, algorithms, bioinformatics, digital image processing and pattern recognition, artificial intelligence, soft computing and applications. Upon a strength review process, a number of high-quality, presenting not only innovative ideas but also a founded evaluation and a strong argumentation of the same, were selected and collected in the present proceedings, that is composed of three different volumes.

Download or read book Robust Texture Features with Applications in Medical Imaging written by Rouzbeh Maani and published by . This book was released on 2015 with total page 190 pages. Available in PDF, EPUB and Kindle. Book excerpt: Image texture is defined as visual patterns appearing in images. The powerful perceptive capability of texture features has made texture analysis a major research topic in computer vision and image processing. Texture features are used to detect defective products in factories, to understand human actions in surveillance systems, to identify people from biometric data (e.g., fingerprint, iris scan, and face photo), and to find abnormality in medical images. Indeed, many advanced applications take a direct or indirect advantage of texture analysis in their processing. An ideal texture feature should not only be discriminative but also be robust to imaging distortions. The developement of robust texture features is first motivated by applying texture analysis to Amyotrophic Lateral Sclerosis (ALS). ALS is a fatal neurodegenerative disease in which evidence of the disease is not perceptible in routine magnetic resonance images (MRI) of the brain even to a trained eye. Unlike brain tumors or multiple sclerosis, the lack of observable features possesses challenges to the detection and diagnosis of ALS. These challenges and the great need in the ALS research community to find a biomarker and to detect the patterns of degeneration in the brain have encouraged the author to study this disease using texture analysis. The results of this thesis suggest texture analysis is a potential biomarker for the disease and hence, open up new avenues towards understanding the disease. This thesis presents a useful approach for texture analysis of the brain. In contrast to the current methods, the proposed approach does not need a region of interest. It performs a voxel based texture analysis and provides a statistical map showing the regions in the brain statistically different between the groups of patients and healthy subjects. A Computer Aided Diagnosis (CAD) tool is developed for this purpose. This toolbox is called the Statistical MAp fRom Texture (SMART) and helps doctors make diagnoses and monitor the progression of diseases using texture analysis. Distortions and effects in real images (e.g., noise, illumination change, blurr effect) increase demand for developing robuts texture features. To address the robustness issues, a novel approach is presented called the Local Frequancy Descriptor (LFD). The LFD is the basis of several novel 2D and 3D texture features presented later in this thesis. It is also the basis of new image gradient operators for 2D and 3D images and a novel image matching method. All texture features, methods, and gradient operators defined based on the LFD show high accuracy and outperform the state-ofthe-art methods. In addition, they present remarkable robutness to imaging effects.

Download or read book Computer Recognition Systems written by Marek Kurzynski and published by Springer. This book was released on 2005-05-04 with total page 903 pages. Available in PDF, EPUB and Kindle. Book excerpt: th This book contains papers accepted for presentation at the 4 International Conference on Computer Recognition Systems CORES'05, May 22-25, 2005, Rydzyna Castle (Poland), This conference is a continuation of a series of con ferences on similar topics (KOSYR) organized each second year, since 1999, by the Chair of Systems and Computer Networks, Wroclaw University of Tech nology. An increasing interest to those conferences paid not only by home but also by foreign participants inspired the organizers to transform them into conferences of international range. Our expectations that the community of specialists in computer recognizing systems will find CORES'05 a proper form of maintaining the tradition of the former conferences have been confirmed by a large number of submitted papers. Alas, organizational constraints caused a necessity to narrow the acceptance criteria so that only 100 papers have been finally included into the conference program. The area covered by accepted papers is still very large and it shows how vivacious is scientific activity in the domain of computer recognition methods and systems. It contains vari ous theoretical approaches to the recognition problem based on mathematical statistics, fuzzy sets, morphological methods, wavelets, syntactic methods, genetic algorithms, artificial neural networks, ontological models, etc. Most attention is still paid to visual objects recognition; however, acoustic, tex tual and other objects are also considered. Among application areas medical problems are in majority; recognition of faces, speech signals and textual in formation processing methods being also investigated.

Download or read book ICT Innovations 2014 written by Ana Madevska Bogdanova and published by Springer. This book was released on 2014-08-09 with total page 370 pages. Available in PDF, EPUB and Kindle. Book excerpt: Data is a common ground, a starting point for each ICT system. Data needs processing, use of different technologies and state-of-the-art methods in order to obtain new knowledge, to develop new useful applications that not only ease, but also increase the quality of life. These applications use the exploration of Big Data, High throughput data, Data Warehouse, Data Mining, Bioinformatics, Robotics, with data coming from social media, sensors, scientific applications, surveillance, video and image archives, internet texts and documents, internet search indexing, medical records, business transactions, web logs, etc. Information and communication technologies have become the asset in everyday life enabling increased level of communication, processing and information exchange. This book offers a collection of selected papers presented at the Sixth International Conference on ICT Innovations held in September 2014, in Ohrid, Macedonia, with main topic World of data. The conference gathered academics, professionals and practitioners in developing solutions and systems in the industrial and business arena, especially innovative commercial implementations, novel applications of technology, and experience in applying recent ICT research advances to practical solutions.

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 Handbook of Texture Analysis written by Ayman El-Baz and published by CRC Press. This book was released on 2024-06-21 with total page 271 pages. Available in PDF, EPUB and Kindle. Book excerpt: The major goals of texture research in computer vision are to understand, model, and process texture and, ultimately, to simulate the human visual learning process using computer technologies. In the last decade, artificial intelligence has been revolutionized by machine learning and big data approaches, outperforming human prediction on a wide range of problems. In particular, deep learning convolutional neural networks (CNNs) are particularly well suited to texture analysis. This volume presents important branches of texture analysis methods which find a proper application in AI-based medical image analysis. This book: Discusses first-order, second-order statistical methods, local binary pattern (LBP) methods, and filter bank-based methods Covers spatial frequency-based methods, Fourier analysis, Markov random fields, Gabor filters, and Hough transformation Describes advanced textural methods based on DL as well as BD and advanced applications of texture to medial image segmentation Is aimed at researchers, academics, and advanced students in biomedical engineering, image analysis, cognitive science, and computer science and engineering This is an essential reference for those looking to advance their understanding in this applied and emergent field.

Download or read book Imaging of Brain Tumors with Histological Correlations written by Antonios Drevelegas and published by Springer Science & Business Media. This book was released on 2013-04-17 with total page 306 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume provides a thorough treatment of the diagnosis of brain tumors by correlating radiographic image features to the underlying pathology. Theoretical considerations and illustrations depicting common and uncommon imaging characteristics of various brain tumors are presented. All modern imaging modalities are used to complete a diagnostic overview of brain tumors with emphasis on recent advances in diagnostic neuroradiology. The book has been designed as a clinical tool for radiologists and other clinicians interested in the current diagnostic approach to brain tumors.

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 2016-03-19 with total page 0 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 An Introduction to Programming with IDL written by Kenneth P. Bowman and published by Elsevier. This book was released on 2006-01-05 with total page 310 pages. Available in PDF, EPUB and Kindle. Book excerpt: In today's information age, scientists and engineers must quickly and efficiently analyze extremely large sets of data. One of the best tools to accomplish this is Interactive Data Language (IDL®), a programming and visualization environment that facilitates numerical modeling, data analysis, and image processing. IDL's high-level language and powerful graphics capabilities allow users to write more flexible programs much faster than is possible with other programming languages. An Introduction to Programming with IDL enables students new to programming, as well as those with experience in other programming languages, to rapidly harness IDL's capabilities: fast, interactive performance; array syntax; dynamic data typing; and built-in graphics. Each concept is illustrated with sample code, including many complete short programs. - Margin notes throughout the text quickly point readers to the relevant sections of IDL manuals - End-of-chapter summaries and exercises help reinforce learning - Students who purchase the book are eligible for a substantial discount on a student version of the IDL software