Artificial Intelligence and Machine Learning in 2D/3D Medical Image Processing

215 Pages·2020·18.385 MB·English

by  Rohit Raja (editor), Sandeep Kumar (editor), Shilpa Rani (editor), K. Ramya Laxmi (editor)

Artificial Intelligence and
Machine Learning in 2D/3D
Medical Image Processing

Artificial Intelligence and
Machine Learning in 2D/3D
Medical Image Processing
Edited by
Rohit Raja, Sandeep Kumar,
Shilpa Rani, and K. Ramya Laxmi
First edition published 2021
by CRC Press
6000 Broken Sound Parkway NW, Suite 300, Boca Raton, FL 33487-2742
and by CRC Press
2 Park Square, Milton Park, Abingdon, Oxon, OX14 4RN
© 2021 Taylor & Francis Group, LLC
CRC Press is an imprint of Taylor & Francis Group, LLC
Reasonable efforts have been made to publish reliable data and information, but the author
and publisher cannot assume responsibility for the validity of all materials or the
consequences of their use. The authors and publishers have attempted to trace the copyright
holders of all material reproduced in this publication and apologize to copyright holders if
permission to publish in this form has not been obtained. If any copyright material has not
been acknowledged please write and let us know so we may rectify in any future reprint.
Except as permitted under U.S. Copyright Law, no part of this book may be reprinted,
reproduced, transmitted, or utilized in any form by any electronic, mechanical, or other
means, now known or hereafter invented, including photocopying, microfilming, and
recording, or in any information storage or retrieval system, without written permission
from the publishers.
For permission to photocopy or use material electronically from this work, access
www.copyright.com or contact the Copyright Clearance Center, Inc. (CCC), 222 Rosewood
Drive, Danvers, MA 01923, 978-750-8400. For works that are not available on CCC please
contact [email protected]
Trademark notice: Product or corporate names may be trademarks or registered trademarks,
and are used only for identification and explanation without intent to infringe.
Library of Congress Cataloging‑in‑Publication Data
Names: Raja, Rohit, editor.
Title: Artificial intelligence and machine learning in 2D/3D medical image processing/
edited by Rohit Raja, Sandeep Kumar, Shilpa Rani, K. Ramya Laxmi.
Description: First edition. | Boca Raton: CRC Press, 2021. | Includes bibliographical
references and index. | Summary: “Medical image fusion is a process which merges
information from multiple images of the same scene. The fused image provides
appended information that can be utilized for more precise localization of
abnormalities. The use of medical image processing databases will help to create and
develop more accurate and diagnostic tools’‐‐ Provided by publisher.
Identifiers: LCCN 2020039590 (print) | LCCN 2020039591 (ebook) | ISBN
9780367374358 (hardback) | ISBN 9780429354526 (ebook)
Subjects: LCSH: Diagnostic imaging‐‐Data processing. | Imaging systems in medicine.
Classification: LCC RC78.7.D53 .A78 2021 (print) | LCC RC78.7.D53 (ebook) | DDC
616.07/540285‐‐dc23
LC record available at https://lccn.loc.gov/2020039590
LC ebook record available at https://lccn.loc.gov/2020039591
ISBN: 978-0-367-37435-8 (hbk)
ISBN: 978-0-429-35452-6 (ebk)
Typeset in Times New Roman
by MPS Limited, Dehradun
Contents
Preface......................................................................................................................vii
Introduction...............................................................................................................ix
Editors......................................................................................................................xv
Contributors...........................................................................................................xvii
Chapter 1 An Introduction to Medical Image Analysis in 3D............................1
Upasana Sinha, Kamal Mehta, and Prakash C. Sharma
Chapter 2 Automated Epilepsy Seizure Detection from EEG Signals
Using Deep CNN Model...................................................................15
Saroj Kumar Pandey, Rekh Ram Janghel, Archana Verma,
Kshitiz Varma, and Pankaj Kumar Mishra
Chapter 3 Medical Image De-Noising Using Combined Bayes Shrink and Total
Variation Techniques.........................................................................31
Devanand Bhonsle, G. R. Sinha, and Vivek Kumar Chandra
Chapter 4 Detection of Nodule and Lung Segmentation Using
Local Gabor XOR Pattern in CT Images.........................................53
Laxmikant Tiwari, Rohit Raja, Vineet Awasthi, and Rohit Miri
Chapter 5 Medical Image Fusion Using Adaptive Neuro
Fuzzy Inference System.....................................................................73
Kamal Mehta, Prakash C. Sharma, and Upasana Sinha
Chapter 6 Medical Imaging in Healthcare Applications...................................97
K. Rawal, G. Sethi, and D. Ghai
Chapter 7 Classification of Diabetic Retinopathy by Applying
an Ensemble of Architectures..........................................................107
Rahul Hooda and Vaishali Devi
Chapter 8 Compression of Clinical Images Using Different
Wavelet Function.............................................................................119
Munish Kumar and Sandeep Kumar
Chapter 9 PSO-Based Optimized Machine Learning Algorithms
for the Prediction of Alzheimer’s Disease......................................133
Saroj Kumar Pandey, Rekh Ram Janghel, Pankaj Kumar Mishra,
Kshitiz Varma, Prashant Kumar, and Saurabh Dewangan
v
vi Contents
Chapter 10 Parkinson’s Disease Detection Using Voice Measurements........143
Raj Kumar Patra, Akanksha Gupta, Maguluri Sudeep Joel, and
Swati Jain
Chapter 11 Speech Impairment Using Hybrid Model of
Machine Learning..........................................................................159
Renuka Arora, Sunny Arora, and Rishu Bhatia
Chapter 12 Advanced Ensemble Machine Learning Model
for Balanced BioAssays.................................................................171
Lokesh Pawar, Anuj Kumar Sharma, Dinesh Kumar,
and Rohit Bajaj
Chapter 13 Lung Segmentation and Nodule Detection in 3D
Medical Images Using Convolution Neural Network..................179
Rohit Raja, Sandeep Kumar, Shilpa Rani, and K. Ramya Laxmi
Index......................................................................................................................189
Preface
In this volume of Medical Image Processing, the study is concerned with interactions
of all forms of radiation and tissue. The development of technology is used to extract
clinically useful information from medical images. Medical Image fusion is a process
which merges information from multiple images of the same setting and the resulting
image retains the most valuable information and features of input images. Medical
Image fusion can extend the range of operations, reduce uncertainties, and expand
reliability. In the Medical Imaging field, different images exist of the same component
of the same patient with dissimilar imaging devices, and the information provided by a
variety of imaging modes is much adulatory each other’s. The fused image provides
appended information that can be utilized for more precise localization of
abnormalities. Image Fusion is a process of combining the relevant information
from a set of images into a single image, such that the resulting fused image will be
more informative and consummate than any of the input images. Image fusion
techniques can improve the quality and increase the application of these data. The
most important applications of the fusion of images include medical imaging, tiny
imaging, remote sensing, computer optics, and robotics. Feature of this book are
• The book highlights the framework of robust and novel methods for medical
image processing techniques.
• Implementation strategies and future research directions meeting the design
and application requirements of several modern and real time applications for
long time.
• The book meets the current needs of the field. Advancement in Artificial
Intelligence and Machine Learning in Medical Image processing are seldom
reviewed in older books.
• Real Time Applications
We express our appreciation to all of the contributing authors who helped us
tremendously with their contributions, time, critical thoughts, and suggestions to put
together this peer-reviewed edited volume. The editors are also thankful to Apple
Academic Press and their team members for the opportunity to publish this volume.
Lastly, we thank our family members for their love, support, encouragement, and
patience during the entire period of this work.
Rohit Raja
Sandeep Kumar
Shilpa Rani
K. Ramya Laxmi
vii

Introduction
The main scope of this volume is to bring together concepts, methods and
applications of medical image processing. The concept of the study is concerned
with the interaction of all forms of radiation and tissue. The development of
technology is used to extract clinically useful information from medical images.
Medical Image Fusion is a process which merges information from multiple images
of the same setting, and the resulting image retains the most valuable information
and features of input images. Medical Image Fusion can extend the range of
operations, reduce uncertainties and expand reliability. In the Medical Imaging
field, different images exist of the same component of the same patient with
dissimilar imaging devices, and the information provided by a variety of imaging
modes is much adulatory each other’s. The fused image provides appended
information that can be utilized for more precise localization of abnormalities.
Image Fusion is a process of combining the relevant information from a set of
images into a single image such that the resulting fused image will be more
informative and consummate than any of the input images alone. Image fusion
techniques can improve the quality and increase the application of these data. The
most important applications of the fusion of images include medical imaging, tiny
imaging, remote sensing, computer optics, and robotics.
This book will target undergraduate graduate and postgraduate students,
researchers, academicians, policy-makers, various government officials,
academicians, technocrats, and industry research professionals who are currently
working in the fields of academia research and the research industry to improve
the quality of healthcare and life expectancy of the general public.
Chapter 1: This chapter introduces Biomedical Image processing, which has
experienced dramatic growth and has been an fascinating area of interdisciplinary
exploration, incorporating knowledge from mathematics, computer sciences,
engineering, statistics, physics, biology, and medicine. Computer-aided
analytical processing has already come to be a vital part of the scientific
process. 3D imaging in medical study is the method used to acquire images of
the body for scientific purpose in order to discover or study diseases. Worldwide,
there are countless imaging strategies performed every week. 3D medical imaging
is efficaciously growing because of developments in image processing strategies,
including image recognition, investigation, and development. Image processing
increases the proportion and extent of detected tissues. Currently, misperception
has been produced among 2D and 3D machinery in health. This section presents
the dissimilarity between these technologies and the software of both simple and
complex image evaluation methods within the medical imaging discipline. This
section also reviews how to demonstrate image interpretation challenges with the
use of unique image processing systems, including division, arrangement, and
registering strategies. Furthermore, it also discusses special kinds of medical
imaging and modalities which contain CT test (pc tomography), MRI (scientific
Resonance Imaging), Ultrasound, X-Ray, and so on. The important goals of this
ix