Image Processing And Acquisition Using Python

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 Introduction
		1.2 What Is Python?
		1.3 Python Environments
			1.3.1 Python Interpreter
			1.3.2 Anaconda Python Distribution
		1.4 Running a Python Program
		1.5 Basic Python Statements and Data Types
			1.5.1 Data Structures
			1.5.2 File Handling
			1.5.3 User-Defined Functions
		1.6 Summary
		1.7 Exercises
	2. Computing using Python Modules
		2.1 Introduction
		2.2 Python Modules
			2.2.1 Creating Modules
			2.2.2 Loading Modules
		2.3 Numpy
			2.3.1 Numpy Array or Matrices?
		2.4 Scipy
		2.5 Matplotlib
		2.6 Python Imaging Library
		2.7 Scikits
		2.8 Python OpenCV Module
		2.9 Summary
		2.10 Exercises
	3. Image and Its Properties
		3.1 Introduction
		3.2 Image and Its Properties
			3.2.1 Bit-Depth
			3.2.2 Pixel and Voxel
			3.2.3 Image Histogram
			3.2.4 Window and Level
			3.2.5 Connectivity: 4 or 8 Pixels
		3.3 Image Types
			3.3.1 JPEG
			3.3.2 TIFF
			3.3.3 DICOM
		3.4 Data Structures for Image Analysis
		3.5 Reading, Writing and Displaying Images
			3.5.1 Reading Images
			3.5.2 Reading DICOM Images using pyDICOM
			3.5.3 Writing Images
			3.5.4 Writing DICOM Images using pyDICOM
			3.5.5 Displaying Images
		3.6 Programming Paradigm
		3.7 Summary
		3.8 Exercises
Part II: Image Processing using Python
	4. Spatial Filters
		4.1 Introduction
		4.2 Filtering
			4.2.1 Mean Filter
			4.2.2 Median Filter
			4.2.3 Max Filter
			4.2.4 Min Filter
		4.3 Edge Detection using Derivatives
			4.3.1 First Derivative Filters
				4.3.1.1 Sobel Filter
				4.3.1.2 Prewitt Filter
				4.3.1.3 Canny Filter
			4.3.2 Second Derivative Filters
				4.3.2.1 Laplacian Filter
				4.3.2.2 Laplacian of Gaussian Filter
		4.4 Shape Detecting Filter
			4.4.1 Frangi Filter
		4.5 Summary
		4.6 Exercises
	5. Image Enhancement
		5.1 Introduction
		5.2 Pixel Transformation
		5.3 Image Inverse
		5.4 Power Law Transformation
		5.5 Log Transformation
		5.6 Histogram Equalization
		5.7 Contrast Limited Adaptive Histogram Equalization (CLAHE)
		5.8 Contrast Stretching
		5.9 Sigmoid Correction
		5.10 Local Contrast Normalization
		5.11 Summary
		5.12 Exercises
	6. Affine Transformation
		6.1 Introduction
		6.2 Affine Transformation
			6.2.1 Translation
			6.2.2 Rotation
			6.2.3 Scaling
			6.2.4 Interpolation
		6.3 Summary
		6.4 Exercises
	7. Fourier Transform
		7.1 Introduction
		7.2 Definition of Fourier Transform
		7.3 Two-Dimensional Fourier Transform
			7.3.1 Fast Fourier Transform using Python
		7.4 Convolution
			7.4.1 Convolution in Fourier Space
		7.5 Filtering in the Frequency Domain
			7.5.1 Ideal Lowpass Filter
			7.5.2 Butterworth Lowpass Filter
			7.5.3 Gaussian Lowpass Filter
			7.5.4 Ideal Highpass Filter
			7.5.5 Butterworth Highpass Filter
			7.5.6 Gaussian Highpass Filter
			7.5.7 Bandpass Filter
		7.6 Summary
		7.7 Exercises
	8. Segmentation
		8.1 Introduction
		8.2 Histogram-Based Segmentation
			8.2.1 Otsu's Method
			8.2.2 Renyi Entropy
			8.2.3 Adaptive Thresholding
		8.3 Region-Based Segmentation
			8.3.1 Watershed Segmentation
		8.4 Contour-Based Segmentation
			8.4.1 Chan-Vese Segmentation
		8.5 Segmentation Algorithm for Various Modalities
			8.5.1 Segmentation of Computed Tomography Image
			8.5.2 Segmentation of MRI Image
			8.5.3 Segmentation of Optical and Electron Microscope Images
		8.6 Summary
		8.7 Exercises
	9. Morphological Operations
		9.1 Introduction
		9.2 History
		9.3 Dilation
		9.4 Erosion
		9.5 Grayscale Dilation and Erosion
		9.6 Opening and Closing
		9.7 Grayscale Opening and Closing
		9.8 Hit-or-Miss
		9.9 Thickening and Thinning
			9.9.1 Skeletonization
		9.10 Summary
		9.11 Exercises
	10. Image Measurements
		10.1 Introduction
		10.2 Labeling
		10.3 Hough Transform
			10.3.1 Hough Line
			10.3.2 Hough Circle
		10.4 Template Matching
		10.5 Corner Detector
			10.5.1 FAST Corner Detector
			10.5.2 Harris Corner Detector
		10.6 Summary
		10.7 Exercises
	11. Neural Network
		11.1 Introduction
		11.2 Introduction
		11.3 Mathematical Modeling
			11.3.1 Forward Propagation
			11.3.2 Back-Propagation
		11.4 Graphical Representation
		11.5 Neural Network for Classification Problems
		11.6 Neural Network Example Code
		11.7 Summary
		11.8 Exercises
	12. Convolutional Neural Network
		12.1 Introduction
		12.2 Convolution
		12.3 Maxpooling
		12.4 LeNet Architecture
		12.5 Summary
		12.6 Exercises
Part III: Image Acquisition
	13. X-Ray and Computed Tomography
		13.1 Introduction
		13.2 History
		13.3 X-Ray Generation
			13.3.1 X-Ray Tube Construction
			13.3.2 X-Ray Generation Process
		13.4 Material Properties
			13.4.1 Attenuation
			13.4.2 Lambert-Beer Law for Multiple Materials
			13.4.3 Factors Determining Attenuation
		13.5 X-Ray Detection
			13.5.1 Image Intensifier
			13.5.2 Multiple-Field II
			13.5.3 Flat Panel Detector (FPD)
		13.6 X-Ray Imaging Modes
			13.6.1 Fluoroscopy
			13.6.2 Angiography
		13.7 Computed Tomography (CT)
			13.7.1 Reconstruction
			13.7.2 Parallel-Beam CT
			13.7.3 Central Slice Theorem
			13.7.4 Fan-Beam CT
			13.7.5 Cone-Beam CT
			13.7.6 Micro-CT
		13.8 Hounsfield Unit (HU)
		13.9 Artifacts
			13.9.1 Geometric Misalignment Artifacts
			13.9.2 Scatter
			13.9.3 Offset and Gain Correction
			13.9.4 Beam Hardening
			13.9.5 Metal Artifacts
		13.10 Summary
		13.11 Exercises
	14. Magnetic Resonance Imaging
		14.1 Introduction
		14.2 Laws Governing NMR and MRI
			14.2.1 Faraday's Law
			14.2.2 Larmor Frequency
			14.2.3 Bloch Equation
		14.3 Material Properties
			14.3.1 Gyromagnetic Ratio
			14.3.2 Proton Density
			14.3.3 T1 and T2 Relaxation Times
		14.4 NMR Signal Detection
		14.5 MRI Signal Detection or MRI Imaging
			14.5.1 Slice Selection
			14.5.2 Phase Encoding
			14.5.3 Frequency Encoding
		14.6 MRI Construction
			14.6.1 Main Magnet
			14.6.2 Gradient Magnet
			14.6.3 RF Coils
			14.6.4 K-Space Imaging
		14.7 T1, T2 and Proton Density Image
		14.8 MRI Modes or Pulse Sequence
			14.8.1 Spin Echo Imaging
			14.8.2 Inversion Recovery
			14.8.3 Gradient Echo Imaging
		14.9 MRI Artifacts
			14.9.1 Motion Artifact
			14.9.2 Metal Artifact
			14.9.3 Inhomogeneity Artifact
			14.9.4 Partial Volume Artifact
		14.10 Summary
		14.11 Exercises
	15. Light Microscopes
		15.1 Introduction
		15.2 Physical Principles
			15.2.1 Geometric Optics
			15.2.2 Numerical Aperture
			15.2.3 Diffraction Limit
			15.2.4 Objective Lens
			15.2.5 Point Spread Function (PSF)
			15.2.6 Wide-Field Microscopes
		15.3 Construction of a Wide-Field Microscope
		15.4 Epi-Illumination
		15.5 Fluorescence Microscope
			15.5.1 Theory
			15.5.2 Properties of Fluorochromes
			15.5.3 Filters
		15.6 Confocal Microscopes
		15.7 Nipkow Disk Microscopes
		15.8 Confocal or Wide-Field?
		15.9 Summary
		15.10 Exercises
	16. Electron Microscopes
		16.1 Introduction
		16.2 Physical Principles
			16.2.1 Electron Beam
			16.2.2 Interaction of Electron with Matter
			16.2.3 Interaction of Electrons in TEM
			16.2.4 Interaction of Electrons in SEM
		16.3 Construction of EMs
			16.3.1 Electron Gun
			16.3.2 Electromagnetic Lens
			16.3.3 Detectors
		16.4 Specimen Preparations
		16.5 Construction of the TEM
		16.6 Construction of the SEM
		16.7 Factors Determining Image Quality
		16.8 Summary
		16.9 Exercises
Appendix A: Process-Based Parallelism using Joblib
	A.1 Introduction to Process-Based Parallelism
	A.2 Introduction to Joblib
	A.3 Parallel Examples
Appendix B: Parallel Programming using MPI4Py
	B.1 Introduction to MPI
	B.2 Need for MPI in Python Image Processing
	B.3 Introduction to MPI4Py
	B.4 Communicator
	B.5 Communication
		B.5.1 Point-to-Point Communication
		B.5.2 Collective Communication
	B.6 Calculating the Value of PI
Appendix C: Introduction to ImageJ
	C.1 Introduction
	C.2 ImageJ Primer
Appendix D: Matlab® and Numpy Functions
	D.1 Introduction
Bibliography
Index