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E-raamat: Image Processing and Acquisition using Python 2nd edition [Taylor & Francis e-raamat]

(SriRav Scientific Solutions, Minneapolis, Minnesota, USA), (University of Minnesota, Minneapolis, USA)
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Image Processing and Acquisition using Python 2nd editionSuurem pilt
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"Image Processing and Acquisition using Python, Second Edition provides readers with a sound foundation in both image acquisition and image processing-one of the first books to integrate these topics. 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 second 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. Features Covers both the physical methods of obtaining images and the analytical processing methods required to understand the science behind the images. Contains many examples, detailed derivations, and working Python examples of the techniques. Offers practical tips on image acquisition and processing. Includes numerous exercises to test reader skills in Python programming and image processing, with solutions to selected problems, example programs, and images available on the book's CRC Press web page. New to this edition Machine learning has become an indispensable part of image processing and computer vision, so in this new edition two new chapters are included: one on neural networks and the other on convolutional neural networks. A new chapter on affine transform and many new algorithms. Updated Python code aligned to the latest version of modules"--

Image Processing and Acquisition using Python, Second Edition provides readers with a sound foundation in both image acquisition and image processing—one of the first books to integrate these topics. 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 second 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.

Features

  • Covers both the physical methods of obtaining images and the analytical processing methods required to understand the science behind the images.

  • Contains many examples, detailed derivations, and working Python examples of the techniques.

  • Offers practical tips on image acquisition and processing.

    • Includes numerous exercises to test reader skills in Python programming and image processing, with solutions to selected problems, example programs, and images available on the book’s CRC Press web page.

    • New to this edition

    • Machine learning has become an indispensable part of image processing and computer vision, so in this new edition two new chapters are included: one on neural networks and the other on convolutional neural networks.

    • A new chapter on affine transform and many new algorithms.

      • Updated Python code aligned to the latest version of modules.

      • Foreword xvii
      Preface xix
      Preface to the First Edition xxi
      Introduction xxv
      Authors xxvii
      List of Symbols and Abbreviations
      		xxix
      I Introduction to Images and Computing using Python
      		1(58)
      1 Introduction to Python
      		3(20)
      1.1 Introduction
      		3(1)
      1.2 What Is Python?
      		3(2)
      1.3 Python Environments
      		5(1)
      1.3.1 Python Interpreter
      		5(1)
      1.3.2 Anaconda Python Distribution
      		6(1)
      1.4 Running a Python Program
      		6(1)
      1.5 Basic Python Statements and Data Types
      		7(12)
      1.5.1 Data Structures
      		11(5)
      1.5.2 File Handling
      		16(2)
      1.5.3 User-Defined Functions
      		18(1)
      1.6 Summary
      		19(1)
      1.7 Exercises
      		20(3)
      2 Computing using Python Modules
      		23(14)
      2.1 Introduction
      		23(1)
      2.2 Python Modules
      		23(3)
      2.2.1 Creating Modules
      		24(1)
      2.2.2 Loading Modules
      		24(2)
      2.3 Numpy
      		26(5)
      2.3.1 Numpy Array or Matrices?
      		30(1)
      2.4 Scipy
      		31(1)
      2.5 Matplotlib
      		31(1)
      2.6 Python Imaging Library
      		32(1)
      2.7 Scikits
      		32(1)
      2.8 Python Open CV Module
      		33(1)
      2.9 Summary
      		34(1)
      2.10 Exercises
      		34(3)
      3 Image and Its Properties
      		37(22)
      3.1 Introduction
      		37(1)
      3.2 Image and Its Properties
      		38(6)
      3.2.1 Bit-Depth
      		38(1)
      3.2.2 Pixel and Voxel
      		39(2)
      3.2.3 Image Histogram
      		41(1)
      3.2.4 Window and Level
      		42(1)
      3.2.5 Connectivity: 4 or 8 Pixels
      		43(1)
      3.3 Image Types
      		44(5)
      3.3.1 JPEG
      		44(1)
      3.3.2 TIFF
      		45(1)
      3.3.3 DICOM
      		45(4)
      3.4 Data Structures for Image Analysis
      		49(1)
      3.5 Reading, Writing and Displaying Images
      		49(4)
      3.5.1 Reading Images
      		49(2)
      3.5.2 Reading DICOM Images using pyDICOM
      		51(1)
      3.5.3 Writing Images
      		52(1)
      3.5.4 Writing DICOM Images using pyDICOM
      		52(1)
      3.5.5 Displaying Images
      		53(1)
      3.6 Programming Paradigm
      		53(3)
      3.7 Summary
      		56(1)
      3.8 Exercises
      		57(2)
      II Image Processing using Python
      		59(216)
      4 Spatial Filters
      		61(34)
      4.1 Introduction
      		61(1)
      4.2 Filtering
      		62(11)
      4.2.1 Mean Filter
      		64(4)
      4.2.2 Median Filter
      		68(2)
      4.2.3 Max Filter
      		70(2)
      4.2.4 Min Filter
      		72(1)
      4.3 Edge Detection using Derivatives
      		73(16)
      4.3.1 First Derivative Filters
      		74(2)
      4.3.1.1 Sobel Filter
      		76(2)
      4.3.1.2 Prewitt Filter
      		78(2)
      4.3.1.3 Canny Filter
      		80(2)
      4.3.2 Second Derivative Filters
      		82(1)
      4.3.2.1 Laplacian Filter
      		83(3)
      4.3.2.2 Laplacian of Gaussian Filter
      		86(3)
      4.4 Shape Detecting Filter
      		89(2)
      4.4.1 Frangi Filter
      		89(2)
      4.5 Summary
      		91(1)
      4.6 Exercises
      		92(3)
      5 Image Enhancement
      		95(28)
      5.1 Introduction
      		95(1)
      5.2 Pixel Transformation
      		95(2)
      5.3 Image Inverse
      		97(2)
      5.4 Power Law Transformation
      		99(3)
      5.5 Log Transformation
      		102(2)
      5.6 Histogram Equalization
      		104(6)
      5.7 Contrast Limited Adaptive Histogram Equalization (CLAHE)
      		110(2)
      5.8 Contrast Stretching
      		112(2)
      5.9 Sigmoid Correction
      		114(3)
      5.10 Local Contrast Normalization
      		117(4)
      5.11 Summary
      		121(1)
      5.12 Exercises
      		121(2)
      6 Affine Transformation
      		123(14)
      6.1 Introduction
      		123(1)
      6.2 Affine Transformation
      		124(10)
      6.2.1 Translation
      		124(3)
      6.2.2 Rotation
      		127(1)
      6.2.3 Scaling
      		128(2)
      6.2.4 Interpolation
      		130(4)
      6.3 Summary
      		134(1)
      6.4 Exercises
      		135(2)
      7 Fourier Transform
      		137(30)
      7.1 Introduction
      		137(1)
      7.2 Definition of Fourier Transform
      		138(3)
      7.3 Two-Dimensional Fourier Transform
      		141(5)
      7.3.1 Fast Fourier Transform using Python
      		143(3)
      7.4 Convolution
      		146(2)
      7.4.1 Convolution in Fourier Space
      		147(1)
      7.5 Filtering in the Frequency Domain
      		148(16)
      7.5.1 Ideal Lowpass Filter
      		148(3)
      7.5.2 Butterworth Lowpass Filter
      		151(1)
      7.5.3 Gaussian Lowpass Filter
      		152(2)
      7.5.4 Ideal Highpass Filter
      		154(3)
      7.5.5 Butterworth Highpass Filter
      		157(2)
      7.5.6 Gaussian Highpass Filter
      		159(1)
      7.5.7 Bandpass Filter
      		160(4)
      7.6 Summary
      		164(1)
      7.7 Exercises
      		164(3)
      8 Segmentation
      		167(28)
      8.1 Introduction
      		167(1)
      8.2 Histogram-Based Segmentation
      		168(11)
      8.2.1 Otsu's Method
      		168(4)
      8.2.2 Renyi Entropy
      		172(4)
      8.2.3 Adaptive Thresholding
      		176(3)
      8.3 Region-Based Segmentation
      		179(8)
      8.3.1 Watershed Segmentation
      		181(6)
      8.4 Contour-Based Segmentation
      		187(4)
      8.4.1 Chan-Vese Segmentation
      		187(4)
      8.5 Segmentation Algorithm for Various Modalities
      		191(2)
      8.5.1 Segmentation of Computed Tomography Image
      		192(1)
      8.5.2 Segmentation of MRI Image
      		192(1)
      8.5.3 Segmentation of Optical and Electron Microscope Images
      		192(1)
      8.6 Summary
      		193(1)
      8.7 Exercises
      		193(2)
      9 Morphological Operations
      		195(32)
      9.1 Introduction
      		195(1)
      9.2 History
      		195(1)
      9.3 Dilation
      		196(5)
      9.4 Erosion
      		201(4)
      9.5 Grayscale Dilation and Erosion
      		205(4)
      9.6 Opening and Closing
      		209(4)
      9.7 Grayscale Opening and Closing
      		213(4)
      9.8 Hit-or-Miss
      		217(4)
      9.9 Thickening and Thinning
      		221(3)
      9.9.1 Skeletonization
      		222(2)
      9.10 Summary
      		224(1)
      9.11 Exercises
      		224(3)
      10 Image Measurements
      		227(24)
      10.1 Introduction
      		227(1)
      10.2 Labeling
      		228(3)
      10.3 Hough Transform
      		231(7)
      10.3.1 Hough Line
      		232(2)
      10.3.2 Hough Circle
      		234(4)
      10.4 Template Matching
      		238(4)
      10.5 Corner Detector
      		242(6)
      10.5.1 FAST Corner Detector
      		243(1)
      10.5.2 Harris Corner Detector
      		244(4)
      10.6 Summary
      		248(1)
      10.7 Exercises
      		249(2)
      11 Neural Network
      		251(14)
      11.1 Introduction
      		251(1)
      11.2 Introduction
      		252(1)
      11.3 Mathematical Modeling
      		252(4)
      11.3.1 Forward Propagation
      		252(2)
      11.3.2 Back-Propagation
      		254(2)
      11.4 Graphical Representation
      		256(3)
      11.5 Neural Network for Classification Problems
      		259(1)
      11.6 Neural Network Example Code
      		259(5)
      11.7 Summary
      		264(1)
      11.8 Exercises
      		264(1)
      12 Convolutional Neural Network
      		265(10)
      12.1 Introduction
      		265(1)
      12.2 Convolution
      		266(1)
      12.3 Maxpooling
      		267(1)
      12.4 LeNet Architecture
      		268(5)
      12.5 Summary
      		273(1)
      12.6 Exercises
      		273(2)
      III Image Acquisition
      		275(106)
      13 X-Ray and Computed Tomography
      		277(38)
      13.1 Introduction
      		277(1)
      13.2 History
      		277(1)
      13.3 X-Ray Generation
      		278(6)
      13.3.1 X-Ray Tube Construction
      		278(2)
      13.3.2 X-Ray Generation Process
      		280(4)
      13.4 Material Properties
      		284(4)
      13.4.1 Attenuation
      		284(2)
      13.4.2 Lambert-Beer Law for Multiple Materials
      		286(1)
      13.4.3 Factors Determining Attenuation
      		287(1)
      13.5 X-Ray Detection
      		288(5)
      13.5.1 Image Intensifier
      		289(1)
      13.5.2 Multiple-Field II
      		290(1)
      13.5.3 Flat Panel Detector (FPD)
      		291(2)
      13.6 X-Ray Imaging Modes
      		293(2)
      13.6.1 Fluoroscopy
      		293(1)
      13.6.2 Angiography
      		293(2)
      13.7 Computed Tomography (CT)
      		295(9)
      13.7.1 Reconstruction
      		295(1)
      13.7.2 Parallel-Beam CT
      		296(1)
      13.7.3 Central Slice Theorem
      		296(4)
      13.7.4 Fan-Beam CT
      		300(1)
      13.7.5 Cone-Beam CT
      		301(2)
      13.7.6 Micro-CT
      		303(1)
      13.8 Hounsfield Unit (HU)
      		304(2)
      13.9 Artifacts
      		306(5)
      13.9.1 Geometric Misalignment Artifacts
      		307(1)
      13.9.2 Scatter
      		307(2)
      13.9.3 Offset and Gain Correction
      		309(1)
      13.9.4 Beam Hardening
      		310(1)
      13.9.5 Metal Artifacts
      		311(1)
      13.10 Summary
      		311(2)
      13.11 Exercises
      		313(2)
      14 Magnetic Resonance Imaging
      		315(28)
      14.1 Introduction
      		315(1)
      14.2 Laws Governing NMR and MRI
      		316(3)
      14.2.1 Faraday's Law
      		316(1)
      14.2.2 Larmor Frequency
      		317(1)
      14.2.3 Bloch Equation
      		318(1)
      14.3 Material Properties
      		319(5)
      14.3.1 Gyromagnetic Ratio
      		319(1)
      14.3.2 Proton Density
      		320(1)
      14.3.3 Ti and T2 Relaxation Times
      		321(3)
      14.4 NMR Signal Detection
      		324(1)
      14.5 MRI Signal Detection or MRI Imaging
      		324(3)
      14.5.1 Slice Selection
      		326(1)
      14.5.2 Phase Encoding
      		326(1)
      14.5.3 Frequency Encoding
      		327(1)
      14.6 MRI Construction
      		327(4)
      14.6.1 Main Magnet
      		328(1)
      14.6.2 Gradient Magnet
      		329(1)
      14.6.3 RF Coils
      		329(1)
      14.6.4 K-Space Imaging
      		330(1)
      14.7 T1, T2 and Proton Density Image
      		331(2)
      14.8 MRI Modes or Pulse Sequence
      		333(3)
      14.8.1 Spin Echo Imaging
      		333(1)
      14.8.2 Inversion Recovery
      		334(1)
      14.8.3 Gradient Echo Imaging
      		335(1)
      14.9 MRI Artifacts
      		336(4)
      14.9.1 Motion Artifact
      		337(2)
      14.9.2 Metal Artifact
      		339(1)
      14.9.3 Inhomogeneity Artifact
      		339(1)
      14.9.4 Partial Volume Artifact
      		340(1)
      14.10 Summary
      		340(1)
      14.11 Exercises
      		341(2)
      15 Light Microscopes
      		343(20)
      15.1 Introduction
      		343(1)
      15.2 Physical Principles
      		344(6)
      15.2.1 Geometric Optics
      		344(1)
      15.2.2 Numerical Aperture
      		345(1)
      15.2.3 Diffraction Limit
      		346(2)
      15.2.4 Objective Lens
      		348(1)
      15.2.5 Point Spread Function (PSF)
      		349(1)
      15.2.6 Wide-Field Microscopes
      		350(1)
      15.3 Construction of a Wide-Field Microscope
      		350(2)
      15.4 Epi-Illumination
      		352(1)
      15.5 Fluorescence Microscope
      		352(4)
      15.5.1 Theory
      		352(1)
      15.5.2 Properties of Fluorochromes
      		353(2)
      15.5.3 Filters
      		355(1)
      15.6 Confocal Microscopes
      		356(1)
      15.7 Nipkow Disk Microscopes
      		357(2)
      15.8 Confocal or Wide-Field?
      		359(1)
      15.9 Summary
      		360(1)
      15.10 Exercises
      		361(2)
      16 Electron Microscopes
      		363(18)
      16.1 Introduction
      		363(1)
      16.2 Physical Principles
      		364(6)
      16.2.1 Electron Beam
      		365(1)
      16.2.2 Interaction of Electron with Matter
      		366(2)
      16.2.3 Interaction of Electrons in TEM
      		368(1)
      16.2.4 Interaction of Electrons in SEM
      		368(2)
      16.3 Construction of EMs
      		370(4)
      16.3.1 Electron Gun
      		370(2)
      16.3.2 Electromagnetic Lens
      		372(1)
      16.3.3 Detectors
      		373(1)
      16.4 Specimen Preparations
      		374(1)
      16.5 Construction of the TEM
      		375(1)
      16.6 Construction of the SEM
      		376(1)
      16.7 Factors Determining Image Quality
      		377(2)
      16.8 Summary
      		379(1)
      16.9 Exercises
      		380(1)
      A Process-Based Parallelism using Joblib
      		381(4)
      A.1 Introduction to Process-Based Parallelism
      		381(1)
      A.2 Introduction to Joblib
      		381(1)
      A.3 Parallel Examples
      		382(3)
      B Parallel Programming using MPI4Py
      		385(10)
      B.1 Introduction to MPI
      		385(1)
      B.2 Need for MPI in Python Image Processing
      		386(1)
      B.3 Introduction to MPI4Py
      		387(1)
      B.4 Communicator
      		388(1)
      B.5 Communication
      		389(4)
      B.5.1 Point-to-Point Communication
      		389(2)
      B.5.2 Collective Communication
      		391(2)
      B.6 Calculating the Value of PI
      		393(2)
      C Introduction to ImageJ
      		395(4)
      C.1 Introduction
      		395(1)
      C.2 ImageJ Primer
      		396(3)
      D MATLAB® and Numpy Functions
      		399(4)
      D.1 Introduction
      		399(4)
      Bibliography 403(14)
      Index 417
      Ravishankar Chityala, Ph.D. is Principal Engineer at IonPath, with eighteen years of experience in image processing. He teaches Python programming and Deep learning using Tensorow at the University of California Santa Cruz, Silicon Valley Campus. Previously, he worked as an image processing consultant at the Minnesota Supercomputing Institute of the University of Minnesota. As an image processing consultant, Dr. Chityala had worked with faculty, students and staff from various departments in the scientific, engineering and medical fields at the University of Minnesota, and his interaction with students had made him aware of their need for greater understanding of and ability to work with image processing and acquisition. Dr. Chityala co-authored Essential Python (Essential Education, California, 2018), also contributed to the writing of Handbook of Physics in Medicine and Biology(CRC Press, Boca Raton, 2009, Robert Splinter). His research interests include image processing, machine learning and deep learning.

      Sridevi Pudipeddi, Ph.D. has eleven years of experience teaching undergraduate courses. She teaches Machine Learning with Python and Python for Data Analysis at the University of California Berkeley at San Francisco campus. Dr. Pudipeddi's research interests are in machine learning, applied mathematics and image and text processing. Python's simple syntax and its vast image processing capabilities, along with the need to understand and quantify important experimental information through image acquisition, have inspired her to co-author this book. Dr. Pudipeddi co authored Essential Python (Essential Education, California, 2018).
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