Image Processing and Acquisition using Python provides readers with a sound foundation in both image acquisition and image processing—one of the first books to integrate these topics together. By improving readers’ knowledge of image acquisition techniques and corresponding image processing, the book will help them perform experiments more effectively and cost efficiently as well as analyze and measure more accurately. Long recognized as one of the easiest languages for non-programmers to learn, Python is used in a variety of practical examples.
A refresher for more experienced readers, the first part of the book presents an introduction to Python, Python modules, reading and writing images using Python, and an introduction to images. The second part discusses the basics of image processing, including pre/post processing using filters, segmentation, morphological operations, and measurements. The last part describes image acquisition using various modalities, such as x-ray, CT, MRI, light microscopy, and electron microscopy. These modalities encompass most of the common image acquisition methods currently used by researchers in academia and industry.
Table of Contents:
Introduction to Images and Computing using Python
Introduction to Python
Introduction
What Is Python?
Python Environments
Running a Python Program
Basic Python Statements and Data Types
Computing using Python Modules
Introduction
Python Modules
Numpy
Scipy
Matplotlib
Python Imaging Library
Scikits
Python OpenCV Module
Image and Its Properties
Introduction
Image and Its Properties
Image Types
Data Structures for Image Analysis
Programming Paradigm
Image Processing using Python
Spatial Filters
Introduction
Filtering
Edge Detection using Derivatives
Image Enhancement
Introduction
Pixel Transformation
Image Inverse
Power Law Transformation
Log Transformation
Histogram Equalization
Contrast Stretching
Fourier Transform
Introduction
Definition of Fourier Transform
Two-Dimensional Fourier Transform
Convolution
Filtering in Frequency Domain
Segmentation
Introduction
Histogram-Based Segmentation
Region-Based Segmentation
Segmentation Algorithm for Various Modalities
Morphological Operations
Introduction
History
Dilation
Erosion
Grayscale Dilation and Erosion
Opening and Closing
Hit-or-Miss
Thickening and Thinning
Image Measurements
Introduction
Labeling
Hough Transform
Template Matching
Image Acquisition
X-Ray and Computed Tomography
Introduction
History
X-Ray Generation
Material Properties
X-Ray Detection
X-Ray Imaging Modes
Computed Tomography (CT)
Hounsfield Unit (HU)
Artifacts
Magnetic Resonance Imaging
Introduction
Laws Governing NMR and MRI
Material Properties
NMR Signal Detection
MRI Signal Detection or MRI Imaging
MRI Construction
T1, T2, and Proton Density Image
MRI Modes or Pulse Sequence
MRI Artifacts
Light Microscopes
Introduction
Physical Principles
Construction of a Wide-Field Microscope
Epi-Illumination
Fluorescence Microscope
Confocal Microscopes
Nipkow Disk Microscopes
Confocal or Wide-Field?
Electron Microscopes
Introduction
Physical Principles
Construction of EM
Specimen Preparations
Construction of TEM
Construction of SEM
Appendix A: Installing Python Distributions
Appendix B: Parallel Programming Using MPI4Py
Appendix C: Introduction to ImageJ
Appendix D: MATLAB and Numpy Functions
Index
A Summary and Exercises appear at the end of each chapter.
Review :
"This multi-disciplinary image processing guide hits the mark when targeting the introductory college-level user who is interested in an open source solution that is scalable. By approaching the topics in a broad and horizontal fashion, Ravi Chityala and Sridevi Pudipeddi have created a very practical resource that should have broad impact and appeal across multiple physical and biological disciplines while using multiple imaging modalities.
This new book uses an intuitive and efficient structure to describe basic acquisition hand-in-hand with image processing topics using the open source Python coding and even provides pre-packed installations. The authors present an effective approach to address the typical requirement of prerequisite image acquisition, computational knowledge, and/or hardware requirements by carefully balancing programming, math, and computer requirements, making image processing accessible to students and high-end users alike in multiple disciplines."
—Mark A. Sanders, Program Director, University Imaging Centers, University of Minnesota
"This is a well-suited companion for any introductory course on image processing. The concepts are clearly explained and well illustrated through examples and Python code provided to the reader. The code allows the reader to readily apply the concepts to any images at hand, which significantly simplifies the understanding of image processing concepts. This is what makes this book great. I recommend this book to researchers and students who are looking for an introduction to image processing and acquisition."
—Martin Styner, University of North Carolina at Chapel Hill
"I am a faculty member with specialization in biomechanics and have often found it hard to conceptualize the fundamentals of image processing. That is until I found this book. Image Processing and Acquisition using Python is unique in that it offers an in-depth understanding of the foundation of mathematics associated with image analysis. Ravi Chityala and Sridevi Pudipeddi provide accessible examples with sample codes to show how the theories are applied. This can be very useful to beginning learners and also for researchers (having Python sample code can be very handy to prototype a solution). This book touches all the fundamental topics on image processing, such as pre/post processing using filters, segmentation, morphological operations, and measurements, and also an in-depth discussion on image acquisition using various modalities like x-ray, CT, MRI, light microscopy, and electron microscopy. All the topics are explained clearly and easily. I would highly recommend this book and cannot praise enough the logical and well-written format that it is presented in."
—Augusto Gil Pascoal, Laboratory of Biomechanics and Functional Morphology, University of Lisbon
"This is a book that every imaging scientist should have on his or her desk … students and researchers need a course or a book to learn both image acquisition and image processing using a single source, and this book, as a well-rounded introduction to both topics, serves that purpose very well. … the authors have done a great job of covering the most commonly used image acquisition modalities … a handy compendium of the most useful information. … As a long-time Perl user, I had no problem installing Python and trying several useful examples from the book."
—From the Foreword by Alexander Zamyatin, Distinguished Scientist, Toshiba Medical Research Institute USA, Inc.
"The parts on Python and image processing are the strengths of the book. I enjoyed these parts and heavily profited from them – in fact they saved me a lot of time – so that I can highly recommend the whole book. The book actually motivated me, as MATLAB® user for two decades, to migrate to Python. Furthermore, I will use this book as textbook for Python and image processing for my future classes in the imaging and processing lab that I teach."
—Professor Andreas Modler, Beuth University for Applied Sciences
