With the recent advances in remote sensing technologies for Earth observation, many different remote sensors are collecting data with distinctive properties. The obtained data are so large and ...complex that analyzing them manually becomes impractical or even impossible. Therefore, understanding remote sensing images effectively, in connection with physics, has been the primary concern of the remote sensing research community in recent years. For this purpose, machine learning is thought to be a promising technique because it can make the system learn to improve itself. With this distinctive characteristic, the algorithms will be more adaptive, automatic, and intelligent. This book introduces some of the most challenging issues of machine learning in the field of remote sensing, and the latest advanced technologies developed for different applications. It integrates with multi-source/multi-temporal/multi-scale data, and mainly focuses on learning to understand remote sensing images. Particularly, it presents many more effective techniques based on the popular concepts of deep learning and big data to reach new heights of data understanding. Through reporting recent advances in the machine learning approaches towards analyzing and understanding remote sensing images, this book can help readers become more familiar with knowledge frontier and foster an increased interest in this field.
With the recent advances in remote sensing technologies for Earth observation, many different remote sensors are collecting data with distinctive properties. The obtained data are so large and ...complex that analyzing them manually becomes impractical or even impossible. Therefore, understanding remote sensing images effectively, in connection with physics, has been the primary concern of the remote sensing research community in recent years. For this purpose, machine learning is thought to be a promising technique because it can make the system learn to improve itself. With this distinctive characteristic, the algorithms will be more adaptive, automatic, and intelligent. This book introduces some of the most challenging issues of machine learning in the field of remote sensing, and the latest advanced technologies developed for different applications. It integrates with multi-source/multi-temporal/multi-scale data, and mainly focuses on learning to understand remote sensing images. Particularly, it presents many more effective techniques based on the popular concepts of deep learning and big data to reach new heights of data understanding. Through reporting recent advances in the machine learning approaches towards analyzing and understanding remote sensing images, this book can help readers become more familiar with knowledge frontier and foster an increased interest in this field.
Remote Sensing Digital Image Analysis provides the non-specialist with an introduction to quantitative evaluation of satellite and aircraft derived remotely retrieved data. Each chapter covers the ...pros and cons of digital remotely sensed data, without detailed mathematical treatment of computer based algorithms, but in a manner conductive to an understanding of their capabilities and limitations. Problems conclude each chapter. This fourth edition has been developed to reflect the changes that have occurred in this area over the past several years. Its focus is on those procedures that seem now to have become part of the set of tools regularly used to perform thematic mapping. As with previous revisions, the fundamental material has been preserved in its original form because of its tutorial value; its style has been revised in places and it has been supplemented if newer aspects have emerged in the time since the third edition appeared. It still meets, however, the needs of the senior student and practitioner.
Image analysis is a fundamental task for extracting information from images acquired across a range of different devices. Since reliable quantitative results are requested, image analysis requires ...highly sophisticated numerical and analytical methods—particularly for applications in medicine, security, and remote sensing, where the results of the processing may consist of vitally important data. The contributions to this book provide a good overview of the most important demands and solutions concerning this research area. In particular, the reader will find image analysis applied for feature extraction, encryption and decryption of data, color segmentation, and in the support new technologies. In all the contributions, entropy plays a pivotal role.
Image Correlation for Shape, Motion and Deformation Measurements provides a comprehensive overview of data extraction through image analysis. Readers will find and in-depth look into various single- ...and multi-camera models (2D-DIC and 3D-DIC), two- and three-dimensional computer vision, and volumetric digital image correlation (VDIC). Fundamentals of accurate image matching are described, along with presentations of both new methods for quantitative error estimates in correlation-based motion measurements, and the effect of out-of-plane motion on 2D measurements. Thorough appendices offer descriptions of continuum mechanics formulations, methods for local surface strain estimation and non-linear optimization, as well as terminology in statistics and probability. With equal treatment of computer vision fundamentals and techniques for practical applications, this volume is both a reference for academic and industry-based researchers and engineers, as well as a valuable companion text for appropriate vision-based educational offerings.
Purpose:
To investigate a measurement method for evaluating the resolution properties of CT imaging systems across reconstruction algorithms, dose, and contrast.
Methods:
An algorithm was developed ...to extract the task-based modulation transfer function (MTF) from disk images generated from the rod inserts in the ACR phantom (model 464 Gammex, WI). These inserts are conventionally employed for HU accuracy assessment. The edge of the disk objects was analyzed to determine the edge-spread function, which was differentiated to yield the line-spread function and Fourier-transformed to generate the object-specific MTF for task-based assessment, denoted MTFTask. The proposed MTF measurement method was validated against the conventional wire technique and further applied to measure the MTF of CT images reconstructed with an adaptive statistical iterative algorithm (ASIR) and a model-based iterative (MBIR) algorithm. Results were further compared to the standard filtered back projection (FBP) algorithm. Measurements were performed and compared across different doses and contrast levels to ascertain the MTFTask dependencies on those factors.
Results:
For the FBP reconstructed images, the MTFTask measured with the inserts were the same as the MTF measured from the wire-based method. For the ASIR and MBIR data, the MTFTask using the high contrast insert was similar to the wire-based MTF and equal or superior to that of FBP. However, results for the MTFTask measured using the low-contrast inserts, the MTFTask for ASIR and MBIR data was lower than for the FBP, which was constant throughout all measurements. Similarly, as a function of mA, the MTFTask for ASIR and MBIR varied as a function of noise–-with MTFTask being proportional to mA. Overall greater variability of MTFTask across dose and contrast was observed for MBIR than for ASIR.
Conclusions:
This approach provides a method for assessing the task-based MTF of a CT system using conventional and iterative reconstructions. Results demonstrated that the object-specific MTF can vary as a function of dose and contrast. The analysis highlighted the paradigm shift for iterative reconstructions when compared to FBP, where iterative reconstructions generally offer superior noise performance but with varying resolution as a function of dose and contrast. The MTFTask generated by this method is expected to provide a more comprehensive assessment of image resolution across different reconstruction algorithms and imaging tasks.
Multislice helical computed tomography scanning offers the advantages of faster acquisition and wide organ coverage for routine clinical diagnostic purposes. However, image reconstruction is faced ...with the challenges of three-dimensional cone-beam geometry, data completeness issues, and low dosage. Of all available reconstruction methods, statistical iterative reconstruction (IR) techniques appear particularly promising since they provide the flexibility of accurate physical noise modeling and geometric system description. In this paper, we present the application of Bayesian iterative algorithms to real 3D multislice helical data to demonstrate significant image quality improvement over conventional techniques. We also introduce a novel prior distribution designed to provide flexibility in its parameters to fine-tune image quality. Specifically, enhanced image resolution and lower noise have been achieved, concurrently with the reduction of helical cone-beam artifacts, as demonstrated by phantom studies. Clinical results also illustrate the capabilities of the algorithm on real patient data. Although computational load remains a significant challenge for practical development, superior image quality combined with advancements in computing technology make IR techniques a legitimate candidate for future clinical applications.
Visual Computing for Medicine, Second Edition, offers cutting-edge visualization techniques and their applications in medical diagnosis, education, and treatment. The book includes algorithms, ...applications, and ideas on achieving reliability of results and clinical evaluation of the techniques covered. Preim and Botha illustrate visualization techniques from research, but also cover the information required to solve practical clinical problems. They base the book on several years of combined teaching and research experience. This new edition includes six new chapters on treatment planning, guidance and training; an updated appendix on software support for visual computing for medicine; and a new global structure that better classifies and explains the major lines of work in the field.
Complete guide to visual computing in medicine, fully revamped and updated with new developments in the fieldIllustrated in full colorIncludes a companion website offering additional content for professors, source code, algorithms, tutorials, videos, exercises, lessons, and more
Purpose:
Different computed tomography (CT) reconstruction techniques offer different image quality attributes of resolution and noise, challenging the ability to compare their dose reduction ...potential against each other. The purpose of this study was to evaluate and compare the task‐based imaging performance of CT systems to enable the assessment of the dose performance of a model‐based iterative reconstruction (MBIR) to that of an adaptive statistical iterative reconstruction (ASIR) and a filtered back projection (FBP) technique.
Methods:
The ACR CT phantom (model 464) was imaged across a wide range of mA setting on a 64‐slice CT scanner (GE Discovery CT750 HD, Waukesha, WI). Based on previous work, the resolution was evaluated in terms of a task‐based modulation transfer function (MTF) using a circular‐edge technique and images from the contrast inserts located in the ACR phantom. Noise performance was assessed in terms of the noise‐power spectrum (NPS) measured from the uniform section of the phantom. The task‐based MTF and NPS were combined with a task function to yield a task‐based estimate of imaging performance, the detectability index (d′). The detectability index was computed as a function of dose for two imaging tasks corresponding to the detection of a relatively small and a relatively large feature (1.5 and 25 mm, respectively). The performance of MBIR in terms of the d′ was compared with that of ASIR and FBP to assess its dose reduction potential.
Results:
Results indicated that MBIR exhibits a variability spatial resolution with respect to object contrast and noise while significantly reducing image noise. The NPS measurements for MBIR indicated a noise texture with a low‐pass quality compared to the typical midpass noise found in FBP‐based CT images. At comparable dose, the d′ for MBIR was higher than those of FBP and ASIR by at least 61% and 19% for the small feature and the large feature tasks, respectively. Compared to FBP and ASIR, MBIR indicated a 46%–84% dose reduction potential, depending on task, without compromising the modeled detection performance.
Conclusions:
The presented methodology based on ACR phantom measurements extends current possibilities for the assessment of CT image quality under the complex resolution and noise characteristics exhibited with statistical and iterative reconstruction algorithms. The findings further suggest that MBIR can potentially make better use of the projections data to reduce CT dose by approximately a factor of 2. Alternatively, if the dose held unchanged, it can improve image quality by different levels for different tasks.
