Yin / Hadjiloucas / Zhang Pattern Classification of Medical Images: Computer Aided Diagnosis

1. Auflage 2017
ISBN: 978-3-319-57027-3
Verlag: Springer International Publishing
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, 218 Seiten, eBook

Reihe: Health Information Science

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.

Zielgruppe

Research

Autoren/Hrsg.

Yin, Xiao-Xia

Hadjiloucas, Sillas

Zhang, Yanchun

Inhaltsverzeichnis

1 Introduction and Motivation for Conducting Medical Image Analysis.- 2 Overview of clinical applications using THz pulse imaging, MRI, OCT and fundus imaging.- 3 Recent Advances in Medical Data Preprocessing and Feature Extraction Techniques.- 4 Pattern Classification.- 5 Introduction to MRI Time Series Image Analysis Techniques.- 6 Outlook for Clifford Algebra Based Feature and Deep Learning AI Architectures.- 7 Concluding remarks.