book

Image Processing and Acquisition using Python, 2nd Edition

Name: Image Processing and Acquisition using Python, 2nd Edition
ISBN: 9780429516528

by Ravishankar Chityala, Sridevi Pudipeddi

June 2020

Intermediate to advanced

452 pages

7h 48m

English

Chapman and Hall/CRC

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Cover
Half Title
Series Page
Title Page
Copyright Page
Dedication
Contents
Foreword
Preface
Preface to the First Edition

Introduction
Authors
List of Symbols and Abbreviations
Part I: Introduction to Images and Computing using Python
1. Introduction to Python
1.1 Introduction1.2 What Is Python?1.3 Python Environments1.3.1 Python Interpreter1.3.2 Anaconda Python Distribution1.4 Running a Python Program1.5 Basic Python Statements and Data Types1.5.1 Data Structures1.5.2 File Handling1.5.3 User-Defined Functions1.6 Summary1.7 Exercises
2. Computing using Python Modules
2.1 Introduction2.2 Python Modules2.2.1 Creating Modules2.2.2 Loading Modules2.3 Numpy2.3.1 Numpy Array or Matrices?2.4 Scipy2.5 Matplotlib2.6 Python Imaging Library2.7 Scikits2.8 Python OpenCV Module2.9 Summary2.10 Exercises
3. Image and Its Properties
3.1 Introduction3.2 Image and Its Properties3.2.1 Bit-Depth3.2.2 Pixel and Voxel3.2.3 Image Histogram3.2.4 Window and Level3.2.5 Connectivity: 4 or 8 Pixels3.3 Image Types3.3.1 JPEG3.3.2 TIFF3.3.3 DICOM3.4 Data Structures for Image Analysis3.5 Reading, Writing and Displaying Images3.5.1 Reading Images3.5.2 Reading DICOM Images using pyDICOM3.5.3 Writing Images3.5.4 Writing DICOM Images using pyDICOM3.5.5 Displaying Images3.6 Programming Paradigm3.7 Summary3.8 Exercises
Part II: Image Processing using Python
4. Spatial Filters
4.1 Introduction4.2 Filtering4.2.1 Mean Filter4.2.2 Median Filter4.2.3 Max Filter4.2.4 Min Filter4.3 Edge Detection using Derivatives4.3.1 First Derivative Filters4.3.1.1 Sobel Filter4.3.1.2 Prewitt Filter4.3.1.3 Canny Filter4.3.2 Second Derivative Filters4.3.2.1 Laplacian Filter4.3.2.2 Laplacian of Gaussian Filter4.4 Shape Detecting Filter4.4.1 Frangi Filter4.5 Summary4.6 Exercises
5. Image Enhancement
5.1 Introduction5.2 Pixel Transformation5.3 Image Inverse5.4 Power Law Transformation5.5 Log Transformation5.6 Histogram Equalization5.7 Contrast Limited Adaptive Histogram Equalization (CLAHE)5.8 Contrast Stretching5.9 Sigmoid Correction5.10 Local Contrast Normalization5.11 Summary5.12 Exercises
6. Affine Transformation
6.1 Introduction6.2 Affine Transformation6.2.1 Translation6.2.2 Rotation6.2.3 Scaling6.2.4 Interpolation6.3 Summary6.4 Exercises
7. Fourier Transform
7.1 Introduction7.2 Definition of Fourier Transform7.3 Two-Dimensional Fourier Transform7.3.1 Fast Fourier Transform using Python7.4 Convolution7.4.1 Convolution in Fourier Space7.5 Filtering in the Frequency Domain7.5.1 Ideal Lowpass Filter7.5.2 Butterworth Lowpass Filter7.5.3 Gaussian Lowpass Filter7.5.4 Ideal Highpass Filter7.5.5 Butterworth Highpass Filter7.5.6 Gaussian Highpass Filter7.5.7 Bandpass Filter7.6 Summary7.7 Exercises
8. Segmentation
8.1 Introduction8.2 Histogram-Based Segmentation8.2.1 Otsu’s Method8.2.2 Renyi Entropy8.2.3 Adaptive Thresholding8.3 Region-Based Segmentation8.3.1 Watershed Segmentation8.4 Contour-Based Segmentation8.4.1 Chan-Vese Segmentation8.5 Segmentation Algorithm for Various Modalities8.5.1 Segmentation of Computed Tomography Image8.5.2 Segmentation of MRI Image8.5.3 Segmentation of Optical and Electron Microscope Images8.6 Summary8.7 Exercises
9. Morphological Operations
9.1 Introduction9.2 History9.3 Dilation9.4 Erosion9.5 Grayscale Dilation and Erosion9.6 Opening and Closing9.7 Grayscale Opening and Closing9.8 Hit-or-Miss9.9 Thickening and Thinning9.9.1 Skeletonization9.10 Summary9.11 Exercises
10. Image Measurements
10.1 Introduction10.2 Labeling10.3 Hough Transform10.3.1 Hough Line10.3.2 Hough Circle10.4 Template Matching10.5 Corner Detector10.5.1 FAST Corner Detector10.5.2 Harris Corner Detector10.6 Summary10.7 Exercises
11. Neural Network
11.1 Introduction11.2 Introduction11.3 Mathematical Modeling11.3.1 Forward Propagation11.3.2 Back-Propagation11.4 Graphical Representation11.5 Neural Network for Classification Problems11.6 Neural Network Example Code11.7 Summary11.8 Exercises
12. Convolutional Neural Network
12.1 Introduction12.2 Convolution12.3 Maxpooling12.4 LeNet Architecture12.5 Summary12.6 Exercises
Part III: Image Acquisition
13. X-Ray and Computed Tomography
13.1 Introduction13.2 History13.3 X-Ray Generation13.3.1 X-Ray Tube Construction13.3.2 X-Ray Generation Process13.4 Material Properties13.4.1 Attenuation13.4.2 Lambert-Beer Law for Multiple Materials13.4.3 Factors Determining Attenuation13.5 X-Ray Detection13.5.1 Image Intensifier13.5.2 Multiple-Field II13.5.3 Flat Panel Detector (FPD)13.6 X-Ray Imaging Modes13.6.1 Fluoroscopy13.6.2 Angiography13.7 Computed Tomography (CT)13.7.1 Reconstruction13.7.2 Parallel-Beam CT13.7.3 Central Slice Theorem13.7.4 Fan-Beam CT13.7.5 Cone-Beam CT13.7.6 Micro-CT13.8 Hounsfield Unit (HU)13.9 Artifacts13.9.1 Geometric Misalignment Artifacts13.9.2 Scatter13.9.3 Offset and Gain Correction13.9.4 Beam Hardening13.9.5 Metal Artifacts13.10 Summary13.11 Exercises
14. Magnetic Resonance Imaging
14.1 Introduction14.2 Laws Governing NMR and MRI14.2.1 Faraday’s Law14.2.2 Larmor Frequency14.2.3 Bloch Equation14.3 Material Properties14.3.1 Gyromagnetic Ratio14.3.2 Proton Density14.3.3 T1 and T2 Relaxation Times14.4 NMR Signal Detection14.5 MRI Signal Detection or MRI Imaging14.5.1 Slice Selection14.5.2 Phase Encoding14.5.3 Frequency Encoding14.6 MRI Construction14.6.1 Main Magnet14.6.2 Gradient Magnet14.6.3 RF Coils14.6.4 K-Space Imaging14.7 T1, T2 and Proton Density Image14.8 MRI Modes or Pulse Sequence14.8.1 Spin Echo Imaging14.8.2 Inversion Recovery14.8.3 Gradient Echo Imaging14.9 MRI Artifacts14.9.1 Motion Artifact14.9.2 Metal Artifact14.9.3 Inhomogeneity Artifact14.9.4 Partial Volume Artifact14.10 Summary14.11 Exercises
15. Light Microscopes
15.1 Introduction15.2 Physical Principles15.2.1 Geometric Optics15.2.2 Numerical Aperture15.2.3 Diffraction Limit15.2.4 Objective Lens15.2.5 Point Spread Function (PSF)15.2.6 Wide-Field Microscopes15.3 Construction of a Wide-Field Microscope15.4 Epi-Illumination15.5 Fluorescence Microscope15.5.1 Theory15.5.2 Properties of Fluorochromes15.5.3 Filters15.6 Confocal Microscopes15.7 Nipkow Disk Microscopes15.8 Confocal or Wide-Field?15.9 Summary15.10 Exercises
16. Electron Microscopes
16.1 Introduction16.2 Physical Principles16.2.1 Electron Beam16.2.2 Interaction of Electron with Matter16.2.3 Interaction of Electrons in TEM16.2.4 Interaction of Electrons in SEM16.3 Construction of EMs16.3.1 Electron Gun16.3.2 Electromagnetic Lens16.3.3 Detectors16.4 Specimen Preparations16.5 Construction of the TEM16.6 Construction of the SEM16.7 Factors Determining Image Quality16.8 Summary16.9 Exercises
Appendix A: Image Processing and Acquisition using Python
A.1 Introduction to Process-Based ParallelismA.2 Introduction to JoblibA.3 Parallel Examples
Appendix B: Image Processing and Acquisition using Python
B.1 Introduction to MPIB.2 Need for MPI in Python Image ProcessingB.3 Introduction to MPI4PyB.4 CommunicatorB.5 CommunicationB.5.1 Point-to-Point CommunicationB.5.2 Collective CommunicationB.6 Calculating the Value of PI
Appendix C: Introduction to ImageJ
C.1 IntroductionC.2 ImageJ Primer
Appendix D: Matlab® and Numpy Functions
D.1 Introduction
Bibliography
Index

Content preview from Image Processing and Acquisition using Python, 2nd Edition

Chapter 12

Convolutional Neural Network

12.1Introduction

The convolution neural network (CNN) is a biologically inspired mathematical model of vision. The journey began successfully with the work by David Hubel and Torsten Wiesel who won the 1981 Nobel prize in Physiology or Medicine for this work. The work by Hubel and Weisel was best summarized by the Nobel committee’s press release ([ppr20]) from 1981. The following paragraph is a reproduction from the press release:

“… the visual cortex’s analysis of the coded message from the retina proceeds as if certain cells read the simple letters in the message and compile them into syllables that are subsequently read by other cells, which, in turn, compile the syllables into words, and these ...

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Image Processing and Acquisition using Python, 2nd Edition

by Ravishankar Chityala, Sridevi Pudipeddi

Become an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month,
and much more.