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Stochastic Modeling for Medical Image Analysis
This book provides a brief introduction to medical imaging, stochastic modeling, and model-guided image analysis. It presents the latest state of the art in stochastic modeling for medical image analysis while incorporating fully tested experimental results throughout. This valuable resource details efficient stochastic modeling techniques, incl
Lung Cancer and Imaging
Lung cancer is one of the most common cancers in both men and women worldwide. Early diagnosis of lung cancer can significantly increase the chances of a patient's survival, yet early detection has historically been difficult. As a result, there has been a great deal of progress in the development of accurate and fast diagnostic tools in recent years. Lung Cancer and Imaging provides an introduction to both the methods currently used in lung cancer diagnosis and the promising new techniques that are emerging. Areas covered include the major trends and challenges in lung cancer detection and diagnosis, classification of cancer types, lung feature extraction in joint PET/CT images, and algorithms in the area of low dosage CT lung cancer images.
Big Data in Multimodal Medical Imaging
There is an urgent need to develop and integrate new statistical, mathematical, visualization, and computational models with the ability to analyze Big Data in order to retrieve useful information to aid clinicians in accurately diagnosing and treating patients. The main focus of this book is to review and summarize state-of-the-art big data and deep learning approaches to analyze and integrate multiple data types for the creation of a decision matrix to aid clinicians in the early diagnosis and identification of high risk patients for human diseases and disorders. Leading researchers will contribute original research book chapters analyzing efforts to solve these important problems.
Machine Learning in Medicine
Machine Learning in Medicine covers the state-of-the-art techniques of machine learning and their applications in the medical field. It presents several computer-aided diagnosis (CAD) systems, which have played an important role in the diagnosis of several diseases in the past decade, e.g., cancer detection, resulting in the development of several successful systems. New developments in machine learning may make it possible in the near future to develop machines that are capable of completely performing tasks that currently cannot be completed without human aid, especially in the medical field. This book covers such machines, including convolutional neural networks (CNNs) with different acti...
Lung Imaging and Computer Aided Diagnosis
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 artifac...
Lung Imaging and CADx
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 sta...
Detection Systems in Lung Cancer and Imaging, Volume 1
This book focuses on major trends and challenges in the detection of lung cancer, presenting work aimed at identifying new techniques and their use in biomedical analysis. This volume covers recent advancements in lung cancer and imaging detection and classification, examining the main applications of Computer aided diagnosis (CAD) relating to lung cancer: lung nodule segmentation, lung nodule classification, and Big Data in lung cancer. Ideal for academics working in lung cancer, data-mining, machine learning, deep learning and reinforcement learning, as well as industry professionals working in the areas of healthcare, lung cancer imaging, machine learning, deep learning and reinforcement learning, this edited collection comprises an essential reference for researchers at the forefront of the field, and provides a high-level entry point for more advanced students. Key Features: -Unique focus on advance work in detection system and classification systems. -An updated reference for lung cancer detection via imaging. -Focus on progressive deep learning and machine learning applications for more effective detection.
Diabetes and Fundus OCT
Diabetes and Fundus OCT brings together a stellar cast of authors who review the computer-aided diagnostic (CAD) systems developed to diagnose non-proliferative diabetic retinopathy in an automated fashion using Fundus and OCTA images. Academic researchers, bioengineers, new investigators and students interested in diabetes and retinopathy need an authoritative reference to bring this multidisciplinary field together to help reduce the amount of time spent on source-searching and instead focus on actual research and the clinical application. This reference depicts the current clinical understanding of diabetic retinopathy, along with the many scientific advances in understanding this conditi...
Prostate Cancer Imaging
This book covers novel strategies and state of the art approaches for automated non-invasive systems for early prostate cancer diagnosis. Prostate cancer is the most frequently diagnosed malignancy after skin cancer and the second leading cause of cancer related male deaths in the USA after lung cancer. However, early detection of prostate cancer increases chances of patients’ survival. Generally, The CAD systems analyze the prostate images in three steps: (i) prostate segmentation; (ii) Prostate description or feature extraction; and (iii) classification of the prostate status. Explores all of the latest research and developments in state-of-the art imaging of the prostate from world class experts. Contains a comprehensive overview of 2D/3D Shape Modeling for MRI data. Presents a detailed examination of automated segmentation of the prostate in 3D imaging. Examines Computer-Aided-Diagnosis through automated techniques. There will be extensive references at the end of each chapter to enhance further study.
Multi Modality State-of-the-Art Medical Image Segmentation and Registration Methodologies
With the advances in image guided surgery for cancer treatment, the role of image segmentation and registration has become very critical. The central engine of any image guided surgery product is its ability to quantify the organ or segment the organ whether it is a magnetic resonance imaging (MRI) and computed tomography (CT), X-ray, PET, SPECT, Ultrasound, and Molecular imaging modality. Sophisticated segmentation algorithms can help the physicians delineate better the anatomical structures present in the input images, enhance the accuracy of medical diagnosis and facilitate the best treatment planning system designs. The focus of this book in towards the state of the art techniques in the area of image segmentation and registration.
