Update cookies preferences

Image Processing and Acquisition using Python [Hardback]

(University of Minnesota, Minneapolis, USA), (SriRav Scientific Solutions, Minneapolis, Minnesota, USA)
  • Format: Hardback, 390 pages, height x width: 235x156 mm, weight: 680 g, 20 Tables, black and white; 135 Illustrations, black and white
  • Series: Chapman & Hall/CRC The Python Series
  • Pub. Date: 19-Feb-2014
  • Publisher: CRC Press Inc
  • ISBN-10: 1466583754
  • ISBN-13: 9781466583757
Other books in subject:
  • Hardback
  • Price: 124,75 €*
  • * This title is out of print. Used copies may be available, but delivery only inside Baltic States
  • This title is out of print. Used copies may be available, but delivery only inside Baltic States.
  • Quantity:
  • Add to basket
  • Add to Wishlist
  • Format: Hardback, 390 pages, height x width: 235x156 mm, weight: 680 g, 20 Tables, black and white; 135 Illustrations, black and white
  • Series: Chapman & Hall/CRC The Python Series
  • Pub. Date: 19-Feb-2014
  • Publisher: CRC Press Inc
  • ISBN-10: 1466583754
  • ISBN-13: 9781466583757
Other books in subject:
"If title belongs to a series, the exact series title that will appear in the book is: Series number, exactly as it will appear in the book: Will the first-named author listed in Author Information appear as the first-named author for all other volumes of the series? "--

"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, Pythonis 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"--



Reviews

"This multi-disciplinary image processing guide hits the mark when targeting the introductory college-level user who is interested in an open source solution that is scalable. By approaching the topics in a broad and horizontal fashion, Ravi Chityala and Sridevi Pudipeddi have created a very practical resource that should have broad impact and appeal across multiple physical and biological disciplines while using multiple imaging modalities.

This new book uses an intuitive and efficient structure to describe basic acquisition hand-in-hand with image processing topics using the open source Python coding and even provides pre-packed installations. The authors present an effective approach to address the typical requirement of prerequisite image acquisition, computational knowledge, and/or hardware requirements by carefully balancing programming, math, and computer requirements, making image processing accessible to students and high-end users alike in multiple disciplines." Mark A. Sanders, Program Director, University Imaging Centers, University of Minnesota



"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



"I am a faculty member with specialization in biomechanics and have often found it hard to conceptualize the fundamentals of image processing. That is until I found this book. 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. This can be very useful to beginning learners and also for researchers (having Python sample code can be very handy to prototype a solution). This book touches all the fundamental topics on image processing, such as pre/post processing using filters, segmentation, morphological operations, and measurements, and also an in-depth discussion on image acquisition using various modalities like x-ray, CT, MRI, light microscopy, and electron microscopy. 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, Laboratory of Biomechanics and Functional Morphology, University of Lisbon



"This is a book that every imaging scientist should have on his or her desk students and researchers need a course or a book to learn both image acquisition and image processing using a single source, and this book, as a well-rounded introduction to both topics, serves that purpose very well. the authors have done a great job of covering the most commonly used image acquisition modalities a handy compendium of the most useful information. As a long-time Perl user, I had no problem installing Python and trying several useful examples from the book." From the Foreword by Alexander Zamyatin, Distinguished Scientist, Toshiba Medical Research Institute USA, Inc.



"The parts on Python and image processing are the strengths of the book. I enjoyed these parts and heavily profited from them in fact they saved me a lot of time so that I can highly recommend the whole book. The book actually motivated me, as MATLAB® user for two decades, to migrate to Python. Furthermore, I will use this book as textbook for Python and image processing for my future classes in the imaging and processing lab that I teach." Professor Andreas Modler, Beuth University for Applied Sciences

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(54)
1 Introduction to Python
3(20)
1.1 Introduction
3(1)
1.2 What is Python?
4(1)
1.3 Python Environments
5(3)
1.3.1 Python Interpreter
6(1)
1.3.2 Enthought Python Distribution (EPD)
6(1)
1.3.3 PythonXY
7(1)
1.4 Running a Python Program
8(1)
1.5 Basic Python Statements and Data Types
8(11)
1.5.1 Data Structures
11(8)
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
32(1)
2.6 Python Imaging Library
33(1)
2.7 Scikits
33(1)
2.8 Python OpenCV Module
34(1)
2.9 Summary
34(1)
2.10 Exercises
35(2)
3 Image and its Properties
37(18)
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
44(1)
3.3.3 DICOM
45(4)
3.4 Data Structures for Image Analysis
49(2)
3.4.1 Reading Images
49(1)
3.4.2 Displaying Images
50(1)
3.4.3 Writing Images
50(1)
3.5 Programming Paradigm
51(2)
3.6 Summary
53(1)
3.7 Exercises
53(2)
II Image Processing using Python
55(152)
4 Spatial Filters
57(32)
4.1 Introduction
57(1)
4.2 Filtering
58(11)
4.2.1 Mean Filter
60(4)
4.2.2 Median Filter
64(2)
4.2.3 Max Filter
66(2)
4.2.4 Min Filter
68(1)
4.3 Edge Detection using Derivatives
69(16)
4.3.1 First Derivative Filters
71(8)
4.3.2 Second Derivative Filters
79(6)
4.4 Summary
85(1)
4.5 Exercises
86(3)
5 Image Enhancement
89(20)
5.1 Introduction
89(1)
5.2 Pixel Transformation
89(2)
5.3 Image Inverse
91(1)
5.4 Power Law Transformation
92(5)
5.5 Log Transformation
97(2)
5.6 Histogram Equalization
99(4)
5.7 Contrast Stretching
103(3)
5.8 Summary
106(1)
5.9 Exercises
107(2)
6 Fourier Transform
109(30)
6.1 Introduction
109(1)
6.2 Definition of Fourier Transform
110(3)
6.3 Two-Dimensional Fourier Transform
113(5)
6.3.1 Fast Fourier Transform using Python
115(3)
6.4 Convolution
118(2)
6.4.1 Convolution in Fourier Space
119(1)
6.5 Filtering in Frequency Domain
120(17)
6.5.1 Ideal Lowpass Filter
120(3)
6.5.2 Butterworth Lowpass Filter
123(2)
6.5.3 Gaussian Lowpass Filter
125(2)
6.5.4 Ideal Highpass Filter
127(3)
6.5.5 Butterworth Highpass Filter
130(2)
6.5.6 Gaussian Highpass Filter
132(2)
6.5.7 Bandpass Filter
134(3)
6.6 Summary
137(1)
6.7 Exercises
138(1)
7 Segmentation
139(26)
7.1 Introduction
139(1)
7.2 Histogram Based Segmentation
139(12)
7.2.1 Otsu's Method
141(3)
7.2.2 Renyi Entropy
144(5)
7.2.3 Adaptive Thresholding
149(2)
7.3 Region Based Segmentation
151(10)
7.3.1 Watershed Segmentation
153(8)
7.4 Segmentation Algorithm for Various Modalities
161(1)
7.4.1 Segmentation of Computed Tomography Image
161(1)
7.4.2 Segmentation of MRI Image
161(1)
7.4.3 Segmentation of Optical and Electron Microscope Image
162(1)
7.5 Summary
162(1)
7.6 Exercises
163(2)
8 Morphological Operations
165(24)
8.1 Introduction
165(1)
8.2 History
165(1)
8.3 Dilation
166(5)
8.4 Erosion
171(4)
8.5 Grayscale Dilation and Erosion
175(1)
8.6 Opening and Closing
176(3)
8.7 Hit-or-Miss
179(5)
8.8 Thickening and Thinning
184(2)
8.8.1 Skeletonization
185(1)
8.9 Summary
186(1)
8.10 Exercises
187(2)
9 Image Measurements
189(18)
9.1 Introduction
189(1)
9.2 Labeling
189(5)
9.3 Hough Transform
194(7)
9.3.1 Hough Line
194(3)
9.3.2 Hough Circle
197(4)
9.4 Template Matching
201(4)
9.5 Summary
205(1)
9.6 Exercises
205(2)
III Image Acquisition
207(106)
10 X-Ray and Computed Tomography
209(38)
10.1 Introduction
209(1)
10.2 History
209(1)
10.3 X-Ray Generation
210(6)
10.3.1 X-Ray Tube Construction
210(2)
10.3.2 X-Ray Generation Process
212(4)
10.4 Material Properties
216(3)
10.4.1 Attenuation
216(2)
10.4.2 Lambert Beer Law for Multiple Materials
218(1)
10.5 X-Ray Detection
219(5)
10.5.1 Image Intensifier
220(1)
10.5.2 Multiple-Field II
221(2)
10.5.3 Flat Panel Detector (FPD)
223(1)
10.6 X-Ray Imaging Modes
224(2)
10.6.1 Fluoroscopy
224(1)
10.6.2 Angiography
224(2)
10.7 Computed Tomography (CT)
226(10)
10.7.1 Reconstruction
227(1)
10.7.2 Parallel Beam CT
227(1)
10.7.3 Central Slice Theorem
228(4)
10.7.4 Fan Beam CT
232(1)
10.7.5 Cone Beam CT
233(1)
10.7.6 Micro-CT
234(2)
10.8 Hounsfield Unit (HU)
236(1)
10.9 Artifacts
237(6)
10.9.1 Geometric Misalignment Artifacts
238(1)
10.9.2 Scatter
238(2)
10.9.3 Offset and Gain Correction
240(1)
10.9.4 Beam Hardening
241(1)
10.9.5 Metal Artifacts
242(1)
10.10 Summary
243(1)
10.11 Exercises
244(3)
11 Magnetic Resonance Imaging
247(28)
11.1 Introduction
247(1)
11.2 Laws Governing NMR and MRI
248(3)
11.2.1 Faraday's Law
248(1)
11.2.2 Larmor Frequency
249(1)
11.2.3 Bloch Equation
250(1)
11.3 Material Properties
251(4)
11.3.1 Gyromagnetic Ratio
251(1)
11.3.2 Proton Density
252(1)
11.3.3 T1 and T2 Relaxation Times
253(2)
11.4 NMR Signal Detection
255(1)
11.5 MRI Signal Detection or MRI Imaging
256(3)
11.5.1 Slice Selection
258(1)
11.5.2 Phase Encoding
258(1)
11.5.3 Frequency Encoding
259(1)
11.6 MRI Construction
259(4)
11.6.1 Main Magnet
259(1)
11.6.2 Gradient Magnet
260(1)
11.6.3 RF Coils
261(1)
11.6.4 K-Space Imaging
262(1)
11.7 T1, T2 and Proton Density Image
263(2)
11.8 MRI Modes or Pulse Sequence
265(3)
11.8.1 Spin Echo Imaging
265(1)
11.8.2 Inversion Recovery
266(1)
11.8.3 Gradient Echo Imaging
267(1)
11.9 MRI Artifacts
268(4)
11.9.1 Motion Artifact
269(2)
11.9.2 Metal Artifact
271(1)
11.9.3 Inhomogeneity Artifact
271(1)
11.9.4 Partial Volume Artifact
272(1)
11.10 Summary
272(1)
11.11 Exercises
273(2)
12 Light Microscopes
275(20)
12.1 Introduction
275(1)
12.2 Physical Principles
276(6)
12.2.1 Geometric Optics
276(1)
12.2.2 Numerical Aperture
277(1)
12.2.3 Diffraction Limit
278(2)
12.2.4 Objective Lens
280(1)
12.2.5 Point Spread Function (PSF)
281(1)
12.2.6 Wide-Field Microscopes
282(1)
12.3 Construction of a Wide-Field Microscope
282(2)
12.4 Epi-Illumination
284(1)
12.5 Fluorescence Microscope
284(4)
12.5.1 Theory
284(1)
12.5.2 Properties of Fluorochromes
285(2)
12.5.3 Filters
287(1)
12.6 Confocal Microscopes
288(1)
12.7 Nipkow Disk Microscopes
289(2)
12.8 Confocal or Wide-Field?
291(1)
12.9 Summary
292(1)
12.10 Exercises
293(2)
13 Electron Microscopes
295(18)
13.1 Introduction
295(1)
13.2 Physical Principles
296(5)
13.2.1 Electron Beam
297(1)
13.2.2 Interaction of Electron with Matter
298(1)
13.2.3 Interaction of Electrons in TEM
299(1)
13.2.4 Interaction of Electrons in SEM
300(1)
13.3 Construction of EM
301(5)
13.3.1 Electron Gun
301(2)
13.3.2 Electromagnetic Lens
303(1)
13.3.3 Detectors
304(2)
13.4 Specimen Preparations
306(1)
13.5 Construction of TEM
307(1)
13.6 Construction of SEM
308(1)
13.7 Summary
309(2)
13.8 Exercises
311(2)
A Installing Python Distributions
313(10)
A.1 Windows
313(5)
A.1.1 PythonXY
313(3)
A.1.2 Enthought Python Distribution
316(1)
A.1.3 Updating or Installing New Modules
316(2)
A.2 Mac or Linux
318(5)
A.2.1 Enthought Python Distribution
318(1)
A.2.2 Installing New Modules
318(5)
B Parallel Programming Using MPI4Py
323(10)
B.1 Introduction to MPI
323(1)
B.2 Need for MPI in Python Image Processing
324(1)
B.3 Introduction to MPI4Py
325(1)
B.4 Communicator
326(1)
B.5 Communication
327(4)
B.5.1 Point-to-Point Communication
327(2)
B.5.2 Collective Communication
329(2)
B.6 Calculating the Value of PI
331(2)
C Introduction to ImageJ
333(4)
C.1 Introduction
333(1)
C.2 ImageJ Primer
334(3)
D MATLAB® and Numpy Functions
337(4)
D.1 Introduction
337(4)
Bibliography 341(10)
Index 351