Biomedical Science/Computer Science P u C d h Chapman & Hall/CRC i i p t Mathematical and Computational Imaging Sciences y e a d l d a i I “This is a well-suited companion for any introductory course on image m Image Processing processing. The concepts are clearly explained and well illustrated a through examples and Python code provided to the reader. The code g e allows the reader to readily apply the concepts to any images at hand, and Acquisition P which significantly simplifies the understanding of image processing r concepts. This is what makes this book great. I recommend this book o to researchers and students who are looking for an introduction to c Python e using image processing and acquisition.” s s —Martin Styner, University of North Carolina at Chapel Hill i n “Image Processing and Acquisition using Python is unique in that it g offers an in-depth understanding of the foundation of mathematics a associated with image analysis. Ravi Chityala and Sridevi Pudipeddi n d provide accessible examples with sample codes to show how the A theories are applied. … All the topics are explained clearly and easily. c I would highly recommend this book and cannot praise enough the q logical and well-written format that it is presented in.” u —Augusto Gil Pascoal, University of Lisbon i s i Image Processing and Acquisition using Python provides you with t i o a sound foundation in both image acquisition and image processing, n uniquely integrating these topics. By improving your knowledge of u image acquisition techniques and corresponding image processing, s the book will help you perform experiments more effectively and cost i Ravishankar Chityala n efficiently as well as analyze and measure more accurately. Long g recognized as one of the easiest languages for non-programmers to P Sridevi Pudipeddi learn, Python is used in a variety of practical examples. y t h K19004 o n K19004_Cover.indd 1 12/18/13 8:37 AM Image Processing and Acquisition Python using K19004_FM.indd 1 1/13/14 10:25 AM Chapman & Hall/CRC Mathematical and Computational Imaging Sciences Series Editors Chandrajit Bajaj Guillermo Sapiro Center for Computational Visualization Department of Electrical The University of Texas at Austin and Computer Engineering Duke University Aims and Scope This series aims to capture new developments and summarize what is known over the whole spectrum of mathematical and computational imaging sciences. It seeks to encourage the integration of mathematical, statistical and computational methods in image acquisition and processing by publishing a broad range of textbooks, reference works and handbooks. The titles included in the series are meant to appeal to students, researchers and professionals in the mathematical, statistical and computational sciences, application areas, as well as interdisciplinary researchers involved in the field. The inclusion of concrete examples and applications, and programming code and examples, is highly encouraged. Published Titles Image Processing for Cinema by Marcelo Bertalmío Image Processing and Acquisition using Python by Ravishankar Chityala and Sridevi Pudipeddi Statistical and Computational Methods in Brain Image Analysis by Moo K. Chung Rough Fuzzy Image Analysis: Foundations and Methodologies by Sankar K. Pal and James F. Peters Theoretical Foundations of Digital Imaging Using MATLAB® by Leonid P. Yaroslavsky Proposals for the series should be submitted to the series editors above or directly to: CRC Press, Taylor & Francis Group 3 Park Square, Milton Park, Abingdon, OX14 4RN, UK K19004_FM.indd 2 1/13/14 10:25 AM Chapman & Hall/CRC Mathematical and Computational Imaging Sciences Image Processing and Acquisition Python using Ravishankar Chityala University of Minnesota at Minneapolis USA Sridevi Pudipeddi SriRav Scientific Solutions Minneapolis, Minnesota, USA K19004_FM.indd 3 1/13/14 10:25 AM MATLAB® is a trademark of The MathWorks, Inc. and is used with permission. The MathWorks does not warrant the accuracy of the text or exercises in this book. This book’s use or discussion of MATLAB® software or related products does not constitute endorsement or sponsorship by The MathWorks of a particular pedagogical approach or particular use of the MATLAB® software. 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Visit the Taylor & Francis Web site at http://www.taylorandfrancis.com and the CRC Press Web site at http://www.crcpress.com To our parents and siblings Contents 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 vii viii Contents 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 Contents ix 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