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Ravishankar Chityala & Sridevi Pudipeddi - Image Processing and Acquisition using Python [2014, PDF, ENG]

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Image Processing and Acquisition using Python

Год издания: 2014
Автор: Ravishankar Chityala & Sridevi Pudipeddi
Жанр или тематика: обработка изображений

Издательство: CRC Press, Taylor & Francis Group
ISBN: 978-1-4665-8376-4
Язык: Английский

Формат: PDF
Качество: Издательский макет или текст (eBook)
Интерактивное оглавление: Да
Количество страниц: 388

Описание: “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

“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. … 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, University of Lisbon

Image Processing and Acquisition using Python provides you with
a sound foundation in both image acquisition and image processing,
uniquely integrating these topics. By improving your knowledge of
image acquisition techniques and corresponding image processing,
the book will help you 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.

List of Figures xvii
List of Tables xxiii
Foreword xxv
Preface xxvii
Introduction xxxi
About the Authors xxxiii
List of Symbols and Abbreviations xxxv
I Introduction to Images and Computing using
Python 1
1 Introduction to Python 3
1.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . 3
1.2 What is Python? . . . . . . . . . . . . . . . . . . . . . 4
1.3 Python Environments . . . . . . . . . . . . . . . . . . 5
1.3.1 Python Interpreter . . . . . . . . . . . . . . . . 6
1.3.2 Enthought Python Distribution (EPD) . . . . . 6
1.3.3 PythonXY . . . . . . . . . . . . . . . . . . . . . 7
1.4 Running a Python Program . . . . . . . . . . . . . . . 8
1.5 Basic Python Statements and Data Types . . . . . . . 8
1.5.1 Data Structures . . . . . . . . . . . . . . . . . . 11
1.6 Summary . . . . . . . . . . . . . . . . . . . . . . . . . 19
1.7 Exercises . . . . . . . . . . . . . . . . . . . . . . . . . 20
2 Computing using Python Modules 23
2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . 23
2.2 Python Modules . . . . . . . . . . . . . . . . . . . . . 23
2.2.1 Creating Modules . . . . . . . . . . . . . . . . . 24
2.2.2 Loading Modules . . . . . . . . . . . . . . . . . 24
2.3 Numpy . . . . . . . . . . . . . . . . . . . . . . . . . . 26
2.3.1 Numpy Array or Matrices? . . . . . . . . . . . . 30
2.4 Scipy . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31
2.5 Matplotlib . . . . . . . . . . . . . . . . . . . . . . . . . 32
2.6 Python Imaging Library . . . . . . . . . . . . . . . . . 33
2.7 Scikits . . . . . . . . . . . . . . . . . . . . . . . . . . . 33
2.8 Python OpenCV Module . . . . . . . . . . . . . . . . 34
2.9 Summary . . . . . . . . . . . . . . . . . . . . . . . . . 34
2.10 Exercises . . . . . . . . . . . . . . . . . . . . . . . . . 35
3 Image and its Properties 37
3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . 37
3.2 Image and its Properties . . . . . . . . . . . . . . . . . 38
3.2.1 Bit Depth . . . . . . . . . . . . . . . . . . . . . 38
3.2.2 Pixel and Voxel . . . . . . . . . . . . . . . . . . 39
3.2.3 Image Histogram . . . . . . . . . . . . . . . . . 41
3.2.4 Window and Level . . . . . . . . . . . . . . . . 42
3.2.5 Connectivity: 4 or 8 Pixels . . . . . . . . . . . . 43
3.3 Image Types . . . . . . . . . . . . . . . . . . . . . . . 44
3.3.1 JPEG . . . . . . . . . . . . . . . . . . . . . . . 44
3.3.2 TIFF . . . . . . . . . . . . . . . . . . . . . . . . 44
3.3.3 DICOM . . . . . . . . . . . . . . . . . . . . . . 45
3.4 Data Structures for Image Analysis . . . . . . . . . . . 49
3.4.1 Reading Images . . . . . . . . . . . . . . . . . . 49
3.4.2 Displaying Images . . . . . . . . . . . . . . . . . 50
3.4.3 Writing Images . . . . . . . . . . . . . . . . . . 50
3.5 Programming Paradigm . . . . . . . . . . . . . . . . . 51
3.6 Summary . . . . . . . . . . . . . . . . . . . . . . . . . 53
3.7 Exercises . . . . . . . . . . . . . . . . . . . . . . . . . 53
II Image Processing using Python 55
4 Spatial Filters 57
4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . 57
4.2 Filtering . . . . . . . . . . . . . . . . . . . . . . . . . . 58
4.2.1 Mean Filter . . . . . . . . . . . . . . . . . . . . 60
4.2.2 Median Filter . . . . . . . . . . . . . . . . . . . 64
4.2.3 Max Filter . . . . . . . . . . . . . . . . . . . . . 66
4.2.4 Min Filter . . . . . . . . . . . . . . . . . . . . . 68
4.3 Edge Detection using Derivatives . . . . . . . . . . . . 69
4.3.1 First Derivative Filters . . . . . . . . . . . . . . 71
4.3.2 Second Derivative Filters . . . . . . . . . . . . . 79
4.4 Summary . . . . . . . . . . . . . . . . . . . . . . . . . 85
4.5 Exercises . . . . . . . . . . . . . . . . . . . . . . . . . 86
5 Image Enhancement 89
5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . 89
5.2 Pixel Transformation . . . . . . . . . . . . . . . . . . . 89
5.3 Image Inverse . . . . . . . . . . . . . . . . . . . . . . . 91
5.4 Power Law Transformation . . . . . . . . . . . . . . . 92
5.5 Log Transformation . . . . . . . . . . . . . . . . . . . 97
5.6 Histogram Equalization . . . . . . . . . . . . . . . . . 99
5.7 Contrast Stretching . . . . . . . . . . . . . . . . . . . 103
5.8 Summary . . . . . . . . . . . . . . . . . . . . . . . . . 106
5.9 Exercises . . . . . . . . . . . . . . . . . . . . . . . . . 107
6 Fourier Transform 109
6.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . 109
6.2 Definition of Fourier Transform . . . . . . . . . . . . . 110
6.3 Two-Dimensional Fourier Transform . . . . . . . . . . 113
6.3.1 Fast Fourier Transform using Python . . . . . . 115
6.4 Convolution . . . . . . . . . . . . . . . . . . . . . . . . 118
6.4.1 Convolution in Fourier Space . . . . . . . . . . 119
6.5 Filtering in Frequency Domain . . . . . . . . . . . . . 120
6.5.1 Ideal Lowpass Filter . . . . . . . . . . . . . . . 120
6.5.2 Butterworth Lowpass Filter . . . . . . . . . . . 123
6.5.3 Gaussian Lowpass Filter . . . . . . . . . . . . . 125
6.5.4 Ideal Highpass Filter . . . . . . . . . . . . . . . 127
6.5.5 Butterworth Highpass Filter . . . . . . . . . . . 130
6.5.6 Gaussian Highpass Filter . . . . . . . . . . . . . 132
6.5.7 Bandpass Filter . . . . . . . . . . . . . . . . . . 134
6.6 Summary . . . . . . . . . . . . . . . . . . . . . . . . . 137
6.7 Exercises . . . . . . . . . . . . . . . . . . . . . . . . . 138
7 Segmentation 139
7.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . 139
7.2 Histogram Based Segmentation . . . . . . . . . . . . . 139
7.2.1 Otsu’s Method . . . . . . . . . . . . . . . . . . 141
7.2.2 Renyi Entropy . . . . . . . . . . . . . . . . . . . 144
7.2.3 Adaptive Thresholding . . . . . . . . . . . . . . 149
7.3 Region Based Segmentation . . . . . . . . . . . . . . . 151
7.3.1 Watershed Segmentation . . . . . . . . . . . . . 153
7.4 Segmentation Algorithm for Various Modalities . . . . 161
7.4.1 Segmentation of Computed Tomography Image 161
7.4.2 Segmentation of MRI Image . . . . . . . . . . . 161
7.4.3 Segmentation of Optical and Electron Micro-
scope Image . . . . . . . . . . . . . . . . . . . . 162
7.5 Summary . . . . . . . . . . . . . . . . . . . . . . . . . 162
7.6 Exercises . . . . . . . . . . . . . . . . . . . . . . . . . 163
8 Morphological Operations 165
8.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . 165
8.2 History . . . . . . . . . . . . . . . . . . . . . . . . . . 165
8.3 Dilation . . . . . . . . . . . . . . . . . . . . . . . . . . 166
8.4 Erosion . . . . . . . . . . . . . . . . . . . . . . . . . . 171
8.5 Grayscale Dilation and Erosion . . . . . . . . . . . . . 175
8.6 Opening and Closing . . . . . . . . . . . . . . . . . . . 176
8.7 Hit-or-Miss . . . . . . . . . . . . . . . . . . . . . . . . 179
8.8 Thickening and Thinning . . . . . . . . . . . . . . . . 184
8.8.1 Skeletonization . . . . . . . . . . . . . . . . . . 185
8.9 Summary . . . . . . . . . . . . . . . . . . . . . . . . . 186
8.10 Exercises . . . . . . . . . . . . . . . . . . . . . . . . . 187
9 Image Measurements 189
9.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . 189
9.2 Labeling . . . . . . . . . . . . . . . . . . . . . . . . . . 189
9.3 Hough Transform . . . . . . . . . . . . . . . . . . . . . 194
9.3.1 Hough Line . . . . . . . . . . . . . . . . . . . . 194
9.3.2 Hough Circle . . . . . . . . . . . . . . . . . . . 197
9.4 Template Matching . . . . . . . . . . . . . . . . . . . . 201
9.5 Summary . . . . . . . . . . . . . . . . . . . . . . . . . 205
9.6 Exercises . . . . . . . . . . . . . . . . . . . . . . . . . 205
III Image Acquisition 207
10 X-Ray and Computed Tomography 209
10.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . 209
10.2 History . . . . . . . . . . . . . . . . . . . . . . . . . . 209
10.3 X-Ray Generation . . . . . . . . . . . . . . . . . . . . 210
10.3.1 X-Ray Tube Construction . . . . . . . . . . . . 210
10.3.2 X-Ray Generation Process . . . . . . . . . . . . 212
10.4 Material Properties . . . . . . . . . . . . . . . . . . . . 216
10.4.1 Attenuation . . . . . . . . . . . . . . . . . . . . 216
10.4.2 Lambert Beer Law for Multiple Materials . . . 218
10.5 X-Ray Detection . . . . . . . . . . . . . . . . . . . . . 219
10.5.1 Image Intensifier . . . . . . . . . . . . . . . . . 220
10.5.2 Multiple-Field II . . . . . . . . . . . . . . . . . 221
10.5.3 Flat Panel Detector (FPD) . . . . . . . . . . . . 223
10.6 X-Ray Imaging Modes . . . . . . . . . . . . . . . . . . 224
10.6.1 Fluoroscopy . . . . . . . . . . . . . . . . . . . . 224
10.6.2 Angiography . . . . . . . . . . . . . . . . . . . . 224
10.7 Computed Tomography (CT) . . . . . . . . . . . . . . 226
10.7.1 Reconstruction . . . . . . . . . . . . . . . . . . 227
10.7.2 Parallel Beam CT . . . . . . . . . . . . . . . . . 227
10.7.3 Central Slice Theorem . . . . . . . . . . . . . . 228
10.7.4 Fan Beam CT . . . . . . . . . . . . . . . . . . . 232
10.7.5 Cone Beam CT . . . . . . . . . . . . . . . . . . 233
10.7.6 Micro-CT . . . . . . . . . . . . . . . . . . . . . 234
10.8 Hounsfield Unit (HU) . . . . . . . . . . . . . . . . . . 236
10.9 Artifacts . . . . . . . . . . . . . . . . . . . . . . . . . . 237
10.9.1 Geometric Misalignment Artifacts . . . . . . . . 238
10.9.2 Scatter . . . . . . . . . . . . . . . . . . . . . . . 238
10.9.3 Offset and Gain Correction . . . . . . . . . . . . 240
10.9.4 Beam Hardening . . . . . . . . . . . . . . . . . 241
10.9.5 Metal Artifacts . . . . . . . . . . . . . . . . . . 242
10.10 Summary . . . . . . . . . . . . . . . . . . . . . . . . . 243
10.11 Exercises . . . . . . . . . . . . . . . . . . . . . . . . . 244
11 Magnetic Resonance Imaging 247
11.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . 247
11.2 Laws Governing NMR and MRI . . . . . . . . . . . . 248
11.2.1 Faraday’s Law . . . . . . . . . . . . . . . . . . . 248
11.2.2 Larmor Frequency . . . . . . . . . . . . . . . . . 249
11.2.3 Bloch Equation . . . . . . . . . . . . . . . . . . 250
11.3 Material Properties . . . . . . . . . . . . . . . . . . . . 251
11.3.1 Gyromagnetic Ratio . . . . . . . . . . . . . . . 251
11.3.2 Proton Density . . . . . . . . . . . . . . . . . . 252
11.3.3 T 1 and T 2 Relaxation Times . . . . . . . . . . . 253
11.4 NMR Signal Detection . . . . . . . . . . . . . . . . . . 255
11.5 MRI Signal Detection or MRI Imaging . . . . . . . . . 256
11.5.1 Slice Selection . . . . . . . . . . . . . . . . . . . 258
11.5.2 Phase Encoding . . . . . . . . . . . . . . . . . . 258
11.5.3 Frequency Encoding . . . . . . . . . . . . . . . 259
11.6 MRI Construction . . . . . . . . . . . . . . . . . . . . 259
11.6.1 Main Magnet . . . . . . . . . . . . . . . . . . . 259
11.6.2 Gradient Magnet . . . . . . . . . . . . . . . . . 260
11.6.3 RF Coils . . . . . . . . . . . . . . . . . . . . . . 261
11.6.4 K-Space Imaging . . . . . . . . . . . . . . . . . 262
11.7 T 1 , T 2 and Proton Density Image . . . . . . . . . . . . 263
11.8 MRI Modes or Pulse Sequence . . . . . . . . . . . . . 265
11.8.1 Spin Echo Imaging . . . . . . . . . . . . . . . . 265
11.8.2 Inversion Recovery . . . . . . . . . . . . . . . . 266
11.8.3 Gradient Echo Imaging . . . . . . . . . . . . . . 267
11.9 MRI Artifacts . . . . . . . . . . . . . . . . . . . . . . . 268
11.9.1 Motion Artifact . . . . . . . . . . . . . . . . . . 269
11.9.2 Metal Artifact . . . . . . . . . . . . . . . . . . . 271
11.9.3 Inhomogeneity Artifact . . . . . . . . . . . . . . 271
11.9.4 Partial Volume Artifact . . . . . . . . . . . . . . 272
11.10 Summary . . . . . . . . . . . . . . . . . . . . . . . . . 272
11.11 Exercises . . . . . . . . . . . . . . . . . . . . . . . . . 273
12 Light Microscopes 275
12.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . 275
12.2 Physical Principles . . . . . . . . . . . . . . . . . . . . 276
12.2.1 Geometric Optics . . . . . . . . . . . . . . . . . 276
12.2.2 Numerical Aperture . . . . . . . . . . . . . . . . 277
12.2.3 Diffraction Limit . . . . . . . . . . . . . . . . . 278
12.2.4 Objective Lens . . . . . . . . . . . . . . . . . . 280
12.2.5 Point Spread Function (PSF) . . . . . . . . . . 281
12.2.6 Wide-Field Microscopes . . . . . . . . . . . . . 282
12.3 Construction of a Wide-Field Microscope . . . . . . . 282
12.4 Epi-Illumination . . . . . . . . . . . . . . . . . . . . . 284
12.5 Fluorescence Microscope . . . . . . . . . . . . . . . . . 284
12.5.1 Theory . . . . . . . . . . . . . . . . . . . . . . . 284
12.5.2 Properties of Fluorochromes . . . . . . . . . . . 285
12.5.3 Filters . . . . . . . . . . . . . . . . . . . . . . . 287
12.6 Confocal Microscopes . . . . . . . . . . . . . . . . . . 288
12.7 Nipkow Disk Microscopes . . . . . . . . . . . . . . . . 289
12.8 Confocal or Wide-Field? . . . . . . . . . . . . . . . . . 291
12.9 Summary . . . . . . . . . . . . . . . . . . . . . . . . . 292
12.10 Exercises . . . . . . . . . . . . . . . . . . . . . . . . . 293
13 Electron Microscopes 295
13.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . 295
13.2 Physical Principles . . . . . . . . . . . . . . . . . . . . 296
13.2.1 Electron Beam . . . . . . . . . . . . . . . . . . 297
13.2.2 Interaction of Electron with Matter . . . . . . . 298
13.2.3 Interaction of Electrons in TEM . . . . . . . . . 299
13.2.4 Interaction of Electrons in SEM . . . . . . . . . 300
13.3 Construction of EM . . . . . . . . . . . . . . . . . . . 301
13.3.1 Electron Gun . . . . . . . . . . . . . . . . . . . 301
13.3.2 Electromagnetic Lens . . . . . . . . . . . . . . . 303
13.3.3 Detectors . . . . . . . . . . . . . . . . . . . . . . 304
13.4 Specimen Preparations . . . . . . . . . . . . . . . . . . 306
13.5 Construction of TEM . . . . . . . . . . . . . . . . . . 307
13.6 Construction of SEM . . . . . . . . . . . . . . . . . . . 308
13.7 Summary . . . . . . . . . . . . . . . . . . . . . . . . . 309
13.8 Exercises . . . . . . . . . . . . . . . . . . . . . . . . . 311
A Installing Python Distributions 313
A.1 Windows . . . . . . . . . . . . . . . . . . . . . . . . . 313
A.1.1 PythonXY . . . . . . . . . . . . . . . . . . . . . 313
A.1.2 Enthought Python Distribution . . . . . . . . . 316
A.1.3 Updating or Installing New Modules . . . . . . 316
A.2 Mac or Linux . . . . . . . . . . . . . . . . . . . . . . . 318
A.2.1 Enthought Python Distribution . . . . . . . . . 318
A.2.2 Installing New Modules . . . . . . . . . . . . . . 318
B Parallel Programming Using MPI4Py 323
B.1 Introduction to MPI . . . . . . . . . . . . . . . . . . . 323
B.2 Need for MPI in Python Image Processing . . . . . . . 324
B.3 Introduction to MPI4Py . . . . . . . . . . . . . . . . . 325
B.4 Communicator . . . . . . . . . . . . . . . . . . . . . . 326
B.5 Communication . . . . . . . . . . . . . . . . . . . . . . 327
B.5.1 Point-to-Point Communication . . . . . . . . . 327
B.5.2 Collective Communication . . . . . . . . . . . . 329
B.6 Calculating the Value of PI . . . . . . . . . . . . . . . 331
C Introduction to ImageJ 333
C.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . 333
C.2 ImageJ Primer . . . . . . . . . . . . . . . . . . . . . . 334
D MATLAB R
?
and Numpy Functions 337
D.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . 337
Bibliography 341
Index 351

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