Automatic Segmentation Of Lung Carcinoma Using 3d Texture Features In Co Registered 18 Fdg Pet Ct Images
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Automatic Segmentation Of Lung Carcinoma Using 3d Texture Features In Co Registered 18 Fdg Pet Ct Images
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Author : Daniel Markel
language : en
Publisher:
Release Date : 2011
Automatic Segmentation Of Lung Carcinoma Using 3d Texture Features In Co Registered 18 Fdg Pet Ct Images written by Daniel Markel and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011 with categories.
Automatic Segmentation Of Lung Carcinoma Using Three Dimensional Texture Features In Co Registered 18 Fdg Pet Ct Images
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Author : Daniel Markel
language : en
Publisher:
Release Date : 2011
Automatic Segmentation Of Lung Carcinoma Using Three Dimensional Texture Features In Co Registered 18 Fdg Pet Ct Images written by Daniel Markel and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011 with categories.
Can be as high as 700% by volume. Robust, automated definition of tumor boundarieshas the ability to significantly improve treatment accuracy and efficiency. However, the information provided in computed tomography (CT) is not sensitive enough to differences between tumor and healthy tissue and positron emission tomography (PET) is hampered by blurriness and low resolution. The textural characteristics of thoracic tissue was investigated and compared with those of tumors found within 21 patient PET and CT images in order to enhance the differences and the boundary between cancerous and healthy tissue. A pattern recognition approach was used from these samples to learn the textural characteristics of each and classify voxels as being either normal or abnormal.highest overlap with that of an oncologist's tumor definition.Variability between oncologists in defining the tumor during radiation therapy planningThe approach was compared to a number of alternative methods and found to have the.
Medical Image Understanding And Analysis
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Author : Bartłomiej W. Papież
language : en
Publisher: Springer Nature
Release Date : 2020-07-08
Medical Image Understanding And Analysis written by Bartłomiej W. Papież and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-07-08 with Computers categories.
This book constitutes the refereed proceedings of the 24th Conference on Medical Image Understanding and Analysis, MIUA 2020, held in July 2020. Due to COVID-19 pandemic the conference was held virtually. The 29 full papers and 5 short papers presented were carefully reviewed and selected from 70 submissions. They were organized according to following topical sections: image segmentation; image registration, reconstruction and enhancement; radiomics, predictive models, and quantitative imaging biomarkers; ocular imaging analysis; biomedical simulation and modelling.
Lung Imaging And Cadx
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Author : Ayman El-Baz
language : en
Publisher: CRC Press
Release Date : 2019-04-24
Lung Imaging And Cadx written by Ayman El-Baz and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-04-24 with Technology & Engineering categories.
Developing an effective computer-aided diagnosis (CAD) system for lung cancer is of great clinical importance and can significantly increase the patient's chance for survival. For this reason, CAD systems for lung cancer have been investigated in a large number of research studies. A typical CAD system for lung cancer diagnosis is composed of four main processing steps: segmentation of the lung fields, detection of nodules inside the lung fields, segmentation of the detected nodules, and diagnosis of the nodules as benign or malignant. This book overviews the current state-of-the-art techniques that have been developed to implement each of these CAD processing steps. Overviews the latest state-of-the-art diagnostic CAD systems for lung cancer imaging and diagnosis Offers detailed coverage of 3D and 4D image segmentation Illustrates unique fully automated detection systems coupled with 4D Computed Tomography (CT) Written by authors who are world-class researchers in the biomedical imaging sciences Includes extensive references at the end of each chapter to enhance further study Ayman El-Baz is a professor, university scholar, and chair of the Bioengineering Department at the University of Louisville, Louisville, Kentucky. He earned his bachelor’s and master’s degrees in electrical engineering in 1997 and 2001, respectively. He earned his doctoral degree in electrical engineering from the University of Louisville in 2006. In 2009, he was named a Coulter Fellow for his contributions to the field of biomedical translational research. He has 17 years of hands-on experience in the fields of bio-imaging modeling and noninvasive computer-assisted diagnosis systems. He has authored or coauthored more than 500 technical articles (132 journals, 23 books, 57 book chapters, 211 refereed-conference papers, 137 abstracts, and 27 U.S. patents and disclosures). Jasjit S. Suri is an innovator, scientist, a visionary, an industrialist, and an internationally known world leader in biomedical engineering. He has spent over 25 years in the field of biomedical engineering/devices and its management. He received his doctorate from the University of Washington, Seattle, and his business management sciences degree from Weatherhead School of Management, Case Western Reserve University, Cleveland, Ohio. He was awarded the President’s Gold Medal in 1980 and named a Fellow of the American Institute of Medical and Biological Engineering for his outstanding contributions in 2004. In 2018, he was awarded the Marquis Life Time Achievement Award for his outstanding contributions and dedication to medical imaging and its management.
Pet Ct In Lung Cancer
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Author : Archi Agrawal
language : en
Publisher: Springer
Release Date : 2018-02-16
Pet Ct In Lung Cancer written by Archi Agrawal and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-02-16 with Medical categories.
This concise, excellently illustrated pocket book provides an up-to-date summary of the science and practice of PET/CT imaging in lung cancer. The coverage encompasses the entire spectrum of lung cancer – pathology, radiological and PET/CT imaging, and management. Readers will also find information on the physics of PET and its use in respiratory gating and radiotherapy planning. The highlights of the book are the exquisite depiction of normal variants, pitfalls, and artifacts and a pictorial atlas of the various types of lung cancer and their manifestations. The contributing authors are well-known and experienced oncologists, pathologists, radiologists, and nuclear physicians. This book has been compiled under the auspices of the British Nuclear Medicine Society. It will be of high value for nuclear physicians, radiologists, referring clinicians and oncologists, and paramedical staff working in these fields
Landmarking And Segmentation Of 3d Ct Images
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Author : Shantanu Banik
language : en
Publisher: Morgan & Claypool Publishers
Release Date : 2009-04-08
Landmarking And Segmentation Of 3d Ct Images written by Shantanu Banik and has been published by Morgan & Claypool Publishers this book supported file pdf, txt, epub, kindle and other format this book has been release on 2009-04-08 with Technology & Engineering categories.
Segmentation and landmarking of computed tomographic (CT) images of pediatric patients are important and useful in computer-aided diagnosis (CAD), treatment planning, and objective analysis of normal as well as pathological regions. Identification and segmentation of organs and tissues in the presence of tumors are difficult. Automatic segmentation of the primary tumor mass in neuroblastoma could facilitate reproducible and objective analysis of the tumor's tissue composition, shape, and size. However, due to the heterogeneous tissue composition of the neuroblastic tumor, ranging from low-attenuation necrosis to high-attenuation calcification, segmentation of the tumor mass is a challenging problem. In this context, methods are described in this book for identification and segmentation of several abdominal and thoracic landmarks to assist in the segmentation of neuroblastic tumors in pediatric CT images. Methods to identify and segment automatically the peripheral artifacts and tissues, the rib structure, the vertebral column, the spinal canal, the diaphragm, and the pelvic surface are described. Techniques are also presented to evaluate quantitatively the results of segmentation of the vertebral column, the spinal canal, the diaphragm, and the pelvic girdle by comparing with the results of independent manual segmentation performed by a radiologist. The use of the landmarks and removal of several tissues and organs are shown to assist in limiting the scope of the tumor segmentation process to the abdomen, to lead to the reduction of the false-positive error, and to improve the result of segmentation of neuroblastic tumors. Table of Contents: Introduction to Medical Image Analysis / Image Segmentation / Experimental Design and Database / Ribs, Vertebral Column, and Spinal Canal / Delineation of the Diaphragm / Delineation of the Pelvic Girdle / Application of Landmarking / Concluding Remarks
Auto Segmentation For Radiation Oncology
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Author : Jinzhong Yang
language : en
Publisher: CRC Press
Release Date : 2021-04-18
Auto Segmentation For Radiation Oncology written by Jinzhong Yang and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-04-18 with Science categories.
This book provides a comprehensive introduction to current state-of-the-art auto-segmentation approaches used in radiation oncology for auto-delineation of organs-of-risk for thoracic radiation treatment planning. Containing the latest, cutting edge technologies and treatments, it explores deep-learning methods, multi-atlas-based methods, and model-based methods that are currently being developed for clinical radiation oncology applications. Each chapter focuses on a specific aspect of algorithm choices and discusses the impact of the different algorithm modules to the algorithm performance as well as the implementation issues for clinical use (including data curation challenges and auto-contour evaluations). This book is an ideal guide for radiation oncology centers looking to learn more about potential auto-segmentation tools for their clinic in addition to medical physicists commissioning auto-segmentation for clinical use. Features: Up-to-date with the latest technologies in the field Edited by leading authorities in the area, with chapter contributions from subject area specialists All approaches presented in this book are validated using a standard benchmark dataset established by the Thoracic Auto-segmentation Challenge held as an event of the 2017 Annual Meeting of American Association of Physicists in Medicine
Computer Aided Diagnosis Of Lung Ground Glass Opacity Nodules And Large Lung Cancers In Ct
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Author : Jinghao Zhou
language : en
Publisher:
Release Date : 2008
Computer Aided Diagnosis Of Lung Ground Glass Opacity Nodules And Large Lung Cancers In Ct written by Jinghao Zhou and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2008 with Lungs categories.
Diagnosis of lung nodules and cancers is a critical and urgent problem in clinical diagnosis. This thesis is to design and build a computer aided lung ground glass opacity (GGO) nodules and large lung cancers diagnosis system which aims to quantify the volumetric change of the lung GGO nodules and large lung cancers between the pre-treatment and post-treatment. In order to quantify the volumetric change of the lung nodules and cancers over time, we need segmentation and registration methods to determine the same lung nodule or cancer between the pre-treatment and posttreatment, as well as lung nodules and cancers detection and segmentation methods. For the registration method, segmented pulmonary tubular objects will act as landmarks. We extract the centerlines of 3D tubular objects using improved ridge-based methods for tubular objects segmentation with fully automatic detection of bifurcation points. The detection of bifurcation points ensures the continuity of the centerlines of the tubular objects. Since medical images contain anatomical structures of various shapes, we first perform a pre-selection method to identify the region containing the tubular objects and extract the centerlines of tubular objects by applying intensity ridge tracing method. These steps are based on the eigenanalysis of the Hessian matrix, which provides an estimation of the elongated direction of tubular objects as well as cross-sectional planes orthogonal to tubular objects. While tracing tubular objects, bifurcation points are automatically detected from the cross-sectional planes by applying scan-conversion method or Adaboost algorithm with specially designed steerable filters. For the registration method, we develop a 3D-3D model based rigid registration method based on bifurcation points. We first perform the 3D tubular objects segmentation method to extract the centerlines of tubular organs and radius estimation in both planning and respiration-correlated CT (RCCT) images. This segmentation method automatically detects the bifurcation points by applying Adaboost algorithm with specially designed filters. We then apply a rigid registration method which minimizes the least square error of the corresponding bifurcation points between the planning CT images and the respiration-correlated CT images. For the lung GGO nodules and large lung cancers detection and segmentation, we propose a novel method to automatically detect and segment lung GGO nodules and large lung cancers from chest CT images. For lung GGO nodules detection, we develop a classifier by boosting k-Nearest Neighbor, whose distance measure is the Euclidean distance between the nonparametric density estimates of two regions. We then apply a clustering method to detect the regions of the lung GGO nodules. The detected regions of lung GGO nodules are then automatically segmented by analyzing the 3D texture likelihood map of the region. We also present the statistical validation of the proposed classifier for automatic lung GGO nodules (10 datasets contains 10 GGO nodules) detection as well as the very promising results of automatic lung GGO nodules segmentation. The methods for the detection and segmentation of large lung cancers are similar to the method above. The improvement is that we propose a robust active shape model method for automatic segmentation of lung areas which can be distorted by large lung cancers. We present the statistical validation of the proposed classifier for large lung cancers (10 datasets contains 16 large lung cancers) detection as well as the very promising results of automatic large lung cancers segmentation. The proposed method provides a new powerful tool for automatic detection as well as accurate and reproducible segmentation of lung GGO nodules and large lung cancers.
Head And Neck Tumor Segmentation
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Author : Vincent Andrearczyk
language : en
Publisher: Springer Nature
Release Date : 2021-01-12
Head And Neck Tumor Segmentation written by Vincent Andrearczyk and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-01-12 with Computers categories.
This book constitutes the First 3D Head and Neck Tumor Segmentation in PET/CT Challenge, HECKTOR 2020, which was held in conjunction with the 23rd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2020, in Lima, Peru, in October 2020. The challenge took place virtually due to the COVID-19 pandemic. The 2 full and 8 short papers presented together with an overview paper in this volume were carefully reviewed and selected form numerous submissions. This challenge aims to evaluate and compare the current state-of-the-art methods for automatic head and neck tumor segmentation. In the context of this challenge, a dataset of 204 delineated PET/CT images was made available for training as well as 53 PET/CT images for testing. Various deep learning methods were developed by the participants with excellent results.
Lung Imaging And Computer Aided Diagnosis
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Author : Ayman El-Baz
language : en
Publisher: CRC Press
Release Date : 2011-08-23
Lung Imaging And Computer Aided Diagnosis written by Ayman El-Baz and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011-08-23 with Medical categories.
Lung cancer remains the leading cause of cancer-related deaths worldwide. Early diagnosis can improve the effectiveness of treatment and increase a patient’s chances of survival. Thus, there is an urgent need for new technology to diagnose small, malignant lung nodules early as well as large nodules located away from large diameter airways because the current technology—namely, needle biopsy and bronchoscopy—fail to diagnose those cases. However, the analysis of small, indeterminate lung masses is fraught with many technical difficulties. Often patients must be followed for years with serial CT scans in order to establish a diagnosis, but inter-scan variability, slice selection artifacts, differences in degree of inspiration, and scan angles can make comparing serial scans unreliable. Lung Imaging and Computer Aided Diagnosis brings together researchers in pulmonary image analysis to present state-of-the-art image processing techniques for detecting and diagnosing lung cancer at an early stage. The book addresses variables and discrepancies in scans and proposes ways of evaluating small lung masses more consistently to allow for more accurate measurement of growth rates and analysis of shape and appearance of the detected lung nodules. Dealing with all aspects of image analysis of the data, this book examines: Lung segmentation Nodule segmentation Vessels segmentation Airways segmentation Lung registration Detection of lung nodules Diagnosis of detected lung nodules Shape and appearance analysis of lung nodules Contributors also explore the effective use of these methodologies for diagnosis and therapy in clinical applications. Arguably the first book of its kind to address and evaluate image-based diagnostic approaches for the early diagnosis of lung cancer, Lung Imaging and Computer Aided Diagnosis constitutes a valuable resource for biomedical engineers, researchers, and clinicians in lung disease imaging.