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Über dieses Buch

This book presents advances in biomedical imaging analysis and processing techniques using time dependent medical image datasets for computer aided diagnosis. The analysis of time-series images is one of the most widely appearing problems in science, engineering, and business. In recent years this problem has gained importance due to the increasing availability of more sensitive sensors in science and engineering and due to the wide-spread use of computers in corporations which have increased the amount of time-series data collected by many magnitudes. An important feature of this book is the exploration of different approaches to handle and identify time dependent biomedical images. Biomedical imaging analysis and processing techniques deal with the interaction between all forms of radiation and biological molecules, cells or tissues, to visualize small particles and opaque objects, and to achieve the recognition of biomedical patterns. These are topics of great importance to biomedical science, biology, and medicine. Biomedical imaging analysis techniques can be applied in many different areas to solve existing problems. The various requirements arising from the process of resolving practical problems motivate and expedite the development of biomedical imaging analysis. This is a major reason for the fast growth of the discipline.



Chapter 1. Introduction and Motivation for Conducting Medical Image Analysis

The demand for advanced image analysis techniques stems from the recent proliferation of new biomedical imaging modalities across the electromagnetic spectrum.
Xiao-Xia Yin, Sillas Hadjiloucas, Yanchun Zhang

Chapter 2. Overview of Clinical Applications Using THz Pulse Imaging, MRI, OCT and Fundus Imaging

After establishing the technological aspects of the four different imaging modalities in the previous chapter, this chapter focuses on their biomedical applications.
Xiao-Xia Yin, Sillas Hadjiloucas, Yanchun Zhang

Chapter 3. Recent Advances in Medical Data Preprocessing and Feature Extraction Techniques

This chapter discusses different feature extraction and selection strategies for the four imaging modalities considered.
Xiao-Xia Yin, Sillas Hadjiloucas, Yanchun Zhang

Chapter 4. Pattern Classification

This chapter discusses pattern classification of high dimensional medical datasets of breast, brain, and retinal tissue on the basis of their extracted features. The extracted features can show linear or non-linear separability, furthermore, they may also be multi-modal or highly correlated. Different algorithms that can be used to generate decision boundaries associated with the observed features are considered.
Xiao-Xia Yin, Sillas Hadjiloucas, Yanchun Zhang

Chapter 5. Introduction to MRI Time Series Image Analysis Techniques

This chapter discusses opportunities for spatiotemporal enhancement in DCE-MRIs using a tensorial multi-channel framework.
Xiao-Xia Yin, Sillas Hadjiloucas, Yanchun Zhang

Chapter 6. Outlook for Clifford Algebra Based Feature and Deep Learning AI Architectures

As stated in previous chapters, the interpretation of medical images requires advances in image segmentation and analysis, shape approximation, three-dimensional (3D) modelling, and registration of volumetric data.
Xiao-Xia Yin, Sillas Hadjiloucas, Yanchun Zhang

Chapter 7. Concluding Remarks

This book considers four different imaging modalities THz-TPI, MRI, fundus imaging and OCT. It is shown that, because of the complementarity between THz-TPI and MRI datasets along with OCT techniques, there is potential for developing a unified biomedical signal processing framework. THz-TPI is currently being explored as a viable alternative imaging modality to assess disease progression in a non-invasive manner. DCE-MRI is well established and is regularly used in clinical environments. In contrast, TPI has yet to gain popularity although there is a general recognition of its potential to provide complementary information to clinicians.
Xiao-Xia Yin, Sillas Hadjiloucas, Yanchun Zhang


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