Image processing and acquisition using Python
著者
書誌事項
Image processing and acquisition using Python
（Chapman & Hall/CRC mathematical and computational imaging sciences / Chandrajit Bajaj, Guillermo Sapiro, series editors）
CRC press, c2014
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注記
"CRC Press in an imprint of Taylor & Francis Group, an Informa business" -- T.p verso
Includes bibliographical references (p. 341-350) and index
内容説明・目次
内容説明
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.
目次
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.
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