Automated Brain Lesion Detection And Segmentation Using Mr Images

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Automated Brain Lesion Detection And Segmentation Using Magnetic Resonance Images

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Author : Nooshin Nabizadeh
language : en
Publisher:
Release Date : 2015

Automated Brain Lesion Detection And Segmentation Using Magnetic Resonance Images written by Nooshin Nabizadeh and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015 with categories.

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).

Automated Brain Lesion Detection And Segmentation Using Mr Images

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Author : Nabizadeh Nooshin
language : en
Publisher: LAP Lambert Academic Publishing
Release Date : 2015-07-27

Automated Brain Lesion Detection And Segmentation Using Mr Images written by Nabizadeh Nooshin and has been published by LAP Lambert Academic Publishing this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-07-27 with categories.

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.

Brain Tumor Mri Image Segmentation Using Deep Learning Techniques

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Author : Jyotismita Chaki
language : en
Publisher: Academic Press
Release Date : 2021-11-27

Brain Tumor Mri Image Segmentation Using Deep Learning Techniques written by Jyotismita Chaki and has been published by Academic Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-11-27 with Science categories.

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

Automatic Methods For Multiple Sclerosis New Lesions Detection And Segmentation

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Author : Olivier Commowick
language : en
Publisher: Frontiers Media SA
Release Date : 2023-04-11

Automatic Methods For Multiple Sclerosis New Lesions Detection And Segmentation written by Olivier Commowick and has been published by Frontiers Media SA this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-04-11 with Science categories.

Application Of Emerging Technologies In The Diagnosis And Treatment Of Patients With Brain Tumors New Frontiers In Imaging For Neuro Oncology

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Author : Domenico Aquino
language : en
Publisher: Frontiers Media SA
Release Date : 2025-06-18

Application Of Emerging Technologies In The Diagnosis And Treatment Of Patients With Brain Tumors New Frontiers In Imaging For Neuro Oncology written by Domenico Aquino and has been published by Frontiers Media SA this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-06-18 with Medical categories.

Continued advancements in medical imaging systems have significantly enhanced our ability to timely and accurately visualize body tissues and disease-related processes. Such advancements are gradually responding to a pressing need for personalized medicine, representing an always more pervasive urgency in every medical field; this is all the more true with regard to neuro-oncology, and physicians have now to deal with it. This shift toward precision medicine, defined as the right treatment for the right patient at the right time, has called for innovative approaches to provide aggregation of different techniques, different disciplines, and different professionals, in order to ensure to patients with brain tumors the highest efficacy in both diagnostic and therapeutic capabilities. In this interdisciplinary or cross-disciplinary vision of neuro-oncology, brain imaging represents a compelling source of crucial information used by clinicians and surgeons, and the flourishing of scientific literature based on image post-processing analysis, artificial intelligence, radiomics, and other fast-growing automations in data analysis has been revolutionizing the way of both understanding and applying neuroimaging for treatments. This Research Topic aims to deepen the readers' understanding of novel medical imaging techniques and image-guided procedures for brain tumors’ diagnosis and treatment, or rather integrating these advancements into clinical practice. In this light, it will provide new insights on the latest strides in medical imaging for brain tumors’ diagnosis and therapeutic management. This Research Topic will also focus on the importance of the combination of different techniques from various clinical domains, to fulfill their potential in a pluralist approach that might lead to a more personalized therapy in patients with brain neoplasms; in that regard, of special interest will be the fast-evolving field of artificial intelligence in neuro-oncology and neuro-oncological imaging.

A Novel Skin Lesion Detection Approach Using Neutrosophic Clustering And Adaptive Region Growing In Dermoscopy Images

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Author : Yanhui Guo
language : en
Publisher: Infinite Study
Release Date :

A Novel Skin Lesion Detection Approach Using Neutrosophic Clustering And Adaptive Region Growing In Dermoscopy Images written by Yanhui Guo and has been published by Infinite Study this book supported file pdf, txt, epub, kindle and other format this book has been release on with categories.

This paper proposes novel skin lesion detection based on neutrosophic clustering and adaptive region growing algorithms applied to dermoscopic images, called NCARG. First, the dermoscopic images are mapped into a neutrosophic set domain using the shearlet transform results for the images.

Brainlesion Glioma Multiple Sclerosis Stroke And Traumatic Brain Injuries

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Author : Alessandro Crimi
language : en
Publisher: Springer
Release Date : 2019-02-08

Brainlesion Glioma Multiple Sclerosis Stroke And Traumatic Brain Injuries written by Alessandro Crimi and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-02-08 with Computers categories.

This two-volume set LNCS 11383 and 11384 constitutes revised selected papers from the 4th International MICCAI Brainlesion Workshop, BrainLes 2018, as well as the International Multimodal Brain Tumor Segmentation, BraTS, Ischemic Stroke Lesion Segmentation, ISLES, MR Brain Image Segmentation, MRBrainS18, Computational Precision Medicine, CPM, and Stroke Workshop on Imaging and Treatment Challenges, SWITCH, which were held jointly at the Medical Image Computing for Computer Assisted Intervention Conference, MICCAI, in Granada, Spain, in September 2018. The 92 papers presented in this volume were carefully reviewed and selected from 95 submissions. They were organized in topical sections named: brain lesion image analysis; brain tumor image segmentation; ischemic stroke lesion image segmentation; grand challenge on MR brain segmentation; computational precision medicine; stroke workshop on imaging and treatment challenges.

Classification And Clustering In Biomedical Signal Processing

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Author : Dey, Nilanjan
language : en
Publisher: IGI Global
Release Date : 2016-04-07

Classification And Clustering In Biomedical Signal Processing written by Dey, Nilanjan and has been published by IGI Global this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-04-07 with Technology & Engineering categories.

Advanced techniques in image processing have led to many innovations supporting the medical field, especially in the area of disease diagnosis. Biomedical imaging is an essential part of early disease detection and often considered a first step in the proper management of medical pathological conditions. Classification and Clustering in Biomedical Signal Processing focuses on existing and proposed methods for medical imaging, signal processing, and analysis for the purposes of diagnosing and monitoring patient conditions. Featuring the most recent empirical research findings in the areas of signal processing for biomedical applications with an emphasis on classification and clustering techniques, this essential publication is designed for use by medical professionals, IT developers, and advanced-level graduate students.

Proceedings Of The 9th International Conference On Advanced Intelligent Systems And Informatics 2023

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Author : AboulElla Hassanien
language : en
Publisher: Springer Nature
Release Date : 2023-09-17

Proceedings Of The 9th International Conference On Advanced Intelligent Systems And Informatics 2023 written by AboulElla Hassanien and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-09-17 with Technology & Engineering categories.

This proceedings book constitutes the refereed proceedings of the 9th International Conference on Advanced Intelligent Systems and Informatics (AISI 2023), which took place in Port Said University, Port Said, Egypt, during September 20–22, 2023, Egypt, and is an international interdisciplinary conference that presents a spectrum of scientific research on all aspects of informatics and intelligent systems, technologies, and applications.

Advance Concepts Of Image Processing And Pattern Recognition

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Author : Narendra Kumar
language : en
Publisher: Springer Nature
Release Date : 2022-02-21

Advance Concepts Of Image Processing And Pattern Recognition written by Narendra Kumar and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-02-21 with Computers categories.

The book explains the important concepts and principles of image processing to implement the algorithms and techniques to discover new problems and applications. It contains numerous fundamental and advanced image processing algorithms and pattern recognition techniques to illustrate the framework. It presents essential background theory, shape methods, texture about new methods, and techniques for image processing and pattern recognition. It maintains a good balance between a mathematical background and practical implementation. This book also contains the comparison table and images that are used to show the results of enhanced techniques. This book consists of novel concepts and hybrid methods for providing effective solutions for society. It also includes a detailed explanation of algorithms in various programming languages like MATLAB, Python, etc. The security features of image processing like image watermarking and image encryption etc. are also discussed in this book. This book will be useful for those who are working in the field of image processing, pattern recognition, and security for digital images. This book targets researchers, academicians, industry, and professionals from R&D organizations, and students, healthcare professionals working in the field of medical imaging, telemedicine, cybersecurity, data scientist, artificial intelligence, image processing, digital hospital, intelligent medicine.