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.
Improve the Accurate Detection and Diagnosis of Cancer and Other Diseases Despite the expansion of the CAD field in recent decades, there is currently no single book dedicated to the development and use of CAD systems. Filling this need, Computer-Aided Detection and Diagnosis in Medical Imaging covers the major technical advances and methodologies shaping the development and clinical utility of CAD systems in breast imaging, chest imaging, abdominal imaging, and other emerging applications. After a historical overview of CAD, the book is divided into four sections. The first section presents CAD technologies in breast imaging, which is the most advanced area of CAD application. The second section discusses CAD technologies in chest and abdominal imaging. The third section explores emerging CAD technologies in a wide range of imaging modalities designed to address a variety of diseases. The final section describes the current use of CAD systems in clinical practice as well as how CAD will play an important role in quantitative image biomarkers and imaging genomics research. This book brings together existing and emerging CAD approaches at a level understandable to students, CAD system developers, basic scientists, and physician scientists. Newcomers to CAD research will learn about fundamental aspects in the process of CAD system development. Developers of CAD systems will gain insight on designing new or improved CAD systems. Experienced researchers will get up-to-date information on the latest CAD technologies.
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.
Pulmonary embolism (PE) is an extremely common and highly lethal condition that is a leading cause of death in all age groups. Over the past 10 years, computed tomography (CT) scanners have gained acceptance as a minimally invasive method for diagnosing PE. In this book, a framework for computer-aided diagnosis of PE in contrast-enhanced CT images is presented. It consists of a combination of a method for segmenting the pulmonary arteries (PA), emboli detection methods as well as a scheme for evaluating their performances. The segmentation of the PA serves one of the clot detection methods, and is carried out through a region growing method that makes use of a priori knowledge of vessel topology. Two different approaches for clot detection are proposed: the first one performs clot detection by analyzing the concavities in the segmentation of the pulmonary arterial tree. It works in a semi-automatic way and it enables the detection of thrombi in the larger sections of the PA. The second method does not make use of PA segmentation and is thus fully automatic, enabling detection of clots farther in the vessels. The combination of these methods provides a robust detection technique that can be used as a safeguard by radiologists, or even as preliminary computer-aided diagnosis (CAD) tool. The evaluation of the method is also discussed, and a scheme for measuring its performance is proposed, including a practical approach to making reference detection data, or ground truths, by radiologists.
Hardbound. Over the last decade or so, many investigators have carried out basic studies and clinical applications toward the development of modern computerized schemes for detection and characterization of lesions in radiologic images, based on computer vision and artificial intelligence. These methods and techniques are generally called computer-aided diagnosis (CAD) schemes. The development of CAD has now reached a new phase, since the first commercial unit of detection of breast lesion in mammograms was approved in June 1998 by the FDA for marketing and sale for clinical use.This book, Computer-Aided Diagnosis in Medical Imaging, presents papers from the First International Workshop on Computer-Aided Diagnosis held on September, 1998 at the University of Chicago Downtown Center. The meeting provided a forum for leading researchers and practitioners in this rapidly expanding field, encompassing automated image analysis, quantitation of im
Level set methods are numerical techniques which offer remarkably powerful tools for understanding, analyzing, and computing interface motion in a host of settings. When used for medical imaging analysis and segmentation, the function assigns a label to each pixel or voxel and optimality is defined based on desired imaging properties. This often includes a detection step to extract specific objects via segmentation. This allows for the segmentation and analysis problem to be formulated and solved in a principled way based on well-established mathematical theories. Level set method is a great tool for modeling time varying medical images and enhancement of numerical computations.
"This book provides a comprehensive overview of machine learning research and technology in medical decision-making based on medical images"--Provided by publisher.
As one of the most important tasks in biomedical imaging, image segmentation provides the foundation for quantitative reasoning and diagnostic techniques. A large variety of different imaging techniques, each with its own physical principle and characteristics (e.g., noise modeling), often requires modality-specific algorithmic treatment. In recent years, substantial progress has been made to biomedical image segmentation. Biomedical image segmentation is characterized by several specific factors. This book presents an overview of the advanced segmentation algorithms and their applications.
The six-volume set LNCS 11764, 11765, 11766, 11767, 11768, and 11769 constitutes the refereed proceedings of the 22nd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2019, held in Shenzhen, China, in October 2019. The 539 revised full papers presented were carefully reviewed and selected from 1730 submissions in a double-blind review process. The papers are organized in the following topical sections: Part I: optical imaging; endoscopy; microscopy. Part II: image segmentation; image registration; cardiovascular imaging; growth, development, atrophy and progression. Part III: neuroimage reconstruction and synthesis; neuroimage segmentation; diffusion weighted magnetic resonance imaging; functional neuroimaging (fMRI); miscellaneous neuroimaging. Part IV: shape; prediction; detection and localization; machine learning; computer-aided diagnosis; image reconstruction and synthesis. Part V: computer assisted interventions; MIC meets CAI. Part VI: computed tomography; X-ray imaging.
This issue gives the general radiologist a solid overview of lung cancer imaging techniques. CT screening for lung cancer is discussed, and the evaluation and management of indeterminate pulmonary nodules is reviewed. Revised TNM lung cancer staging, as well as the optimal imaging protocols for lung cancer staging (CT, MR and PET) are thoroughly examined. A multidisciplinary approach to tissue sampling and updated histopathologic classification of lung cancer are discussed. Image-guided ablative therapies for lung cancer are reviewed. Finally, future trends in lung cancer diagnosis and staging and genetics are reviewed, as well as novel biomarkers for lung cancer detection.
Computational Intelligence in Biomedical Imaging is a comprehensive overview of the state-of-the-art computational intelligence research and technologies in biomedical images with emphasis on biomedical decision making. Biomedical imaging offers useful information on patients’ medical conditions and clues to causes of their symptoms and diseases. Biomedical images, however, provide a large number of images which physicians must interpret. Therefore, computer aids are demanded and become indispensable in physicians’ decision making. This book discusses major technical advancements and research findings in the field of computational intelligence in biomedical imaging, for example, computational intelligence in computer-aided diagnosis for breast cancer, prostate cancer, and brain disease, in lung function analysis, and in radiation therapy. The book examines technologies and studies that have reached the practical level, and those technologies that are becoming available in clinical practices in hospitals rapidly such as computational intelligence in computer-aided diagnosis, biological image analysis, and computer-aided surgery and therapy.
This volume comprises of 21 selected chapters, including two overview chapters devoted to abdominal imaging in clinical applications supported computer aided diagnosis approaches as well as different techniques for solving the pectoral muscle extraction problem in the preprocessing part of the CAD systems for detecting breast cancer in its early stage using digital mammograms. The aim of this book is to stimulate further research in medical imaging applications based algorithmic and computer based approaches and utilize them in real-world clinical applications. The book is divided into four parts, Part-I: Clinical Applications of Medical Imaging, Part-II: Classification and clustering, Part-III: Computer Aided Diagnosis (CAD) Tools and Case Studies and Part-IV: Bio-inspiring based Computer Aided diagnosis techniques.
Issues in Diagnostics and Imaging / 2011 Edition is a ScholarlyEditions™ eBook that delivers timely, authoritative, and comprehensive information about Diagnostics and Imaging. The editors have built Issues in Diagnostics and Imaging: 2011 Edition on the vast information databases of ScholarlyNews.™ You can expect the information about Diagnostics and Imaging in this eBook to be deeper than what you can access anywhere else, as well as consistently reliable, authoritative, informed, and relevant. The content of Issues in Diagnostics and Imaging: 2011 Edition has been produced by the world’s leading scientists, engineers, analysts, research institutions, and companies. All of the content is from peer-reviewed sources, and all of it is written, assembled, and edited by the editors at ScholarlyEditions™ and available exclusively from us. You now have a source you can cite with authority, confidence, and credibility. More information is available at http://www.ScholarlyEditions.com/.
This is the ideal resource for all those requiring an authoritative and up-to-date review of imaging appearances of diseases of the lung, pleura and mediastinum. Chest radiography and CT are integrated with other imaging techniques, including MRI and PET, where appropriate. The clinical and pathologic features of different diseases are provided in varying degrees of detail with more in depth coverage given to rarer and less well understood conditions. A single volume, comprehensive reference text on chest radiology.Provides in a single resource all of the information a generalist in diagnostic radiology needs to know. Concisely and clearly written by a team of 4 internationally recognized authors.Avoids the inconsistency, repetition, and unevenness of coverage that is inherent in multi-contributed books. Multimodality coverage integrated throughout every chapter.All of the applicable imaging modalities are covered in a clinically relevant, diagnostically helpful way. Approximately 3,000 high quality, good-sized images.Provides a complete visual guide that the practitioner can refer to for help in interpretation and diagnosis. Covers both common and uncommon disorders.Provides the user with a single comprehensive resource, no need to consult alternative resources. Access the full text online and download images via Expert Consult Access the latest version of the Fleischner Society's glossary of terms for thoracic imaging. Outlines, summary boxes, key points used throughout.Makes content more accessible by highlighting essential information. Brand new color images to illustrate Functional imaging techniques.Many of the new imaging techniques can provide functional as well as anatomic information. Introduction of a second color throughout in summary boxes in order to better highlight key information. There’s a wealth of key information in the summary boxes—will be highlighted more from the narrative text and will therefore be easier to access. Practical tips on identifying anatomic variants and artefacts in order to avoid diagnostic pitfalls.Many misdiagnoses are the result of basic errors in correlating the anatomic changes seen with imaging to their underlying pathologic processes. Latest techniques in CT, MRI and PET as they relate to thoracic diseases. The pace of development in imaging modalities and new applications/refined techniques in existing modalities continues to drive radiology forward as a specialty. Emphasis on cost-effective image/modality selection.Addresses the hugely important issue of cost-containment by emphasizing which imaging modality is helpful and which is not in any given clinical diagnosis. COPD and Diffuse Lung Disease, Small Airway disease chapters extensively up-dated. Access the full text online and download images via Expert Consult Access the latest version of the Fleischner Society's glossary of terms for thoracic imaging.
This book constitutes the refereed proceedings of the First International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI'98, held in Cambridge, MA, USA, in October 1998. The 134 revised papers presented were carefully selected from a total of 243 submissions. The book is divided into topical sections on surgical planning, surgical navigation and measurements, cardiac image analysis, medical robotic systems, surgical systems and simulators, segmentation, computational neuroanatomy, biomechanics, detection in medical images, data acquisition and processing, neurosurgery and neuroscience, shape analysis, feature extraction, registration, and ultrasound.