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, Python is 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.

Table of Contents:
Introduction to Images and Computing using Python Introduction to Python Introduction What Is Python? Python Environments Running a Python Program Basic Python Statements and Data Types Computing using Python Modules Introduction Python Modules Numpy Scipy Matplotlib Python Imaging Library Scikits Python OpenCV Module Image and Its Properties Introduction Image and Its Properties Image Types Data Structures for Image Analysis Programming Paradigm Image Processing using Python Spatial Filters Introduction Filtering Edge Detection using Derivatives Image Enhancement Introduction Pixel Transformation Image Inverse Power Law Transformation Log Transformation Histogram Equalization Contrast Stretching Fourier Transform Introduction Definition of Fourier Transform Two-Dimensional Fourier Transform Convolution Filtering in Frequency Domain Segmentation Introduction Histogram-Based Segmentation Region-Based Segmentation Segmentation Algorithm for Various Modalities Morphological Operations Introduction History Dilation Erosion Grayscale Dilation and Erosion Opening and Closing Hit-or-Miss Thickening and Thinning Image Measurements Introduction Labeling Hough Transform Template Matching Image Acquisition X-Ray and Computed Tomography Introduction History X-Ray Generation Material Properties X-Ray Detection X-Ray Imaging Modes Computed Tomography (CT) Hounsfield Unit (HU) Artifacts Magnetic Resonance Imaging Introduction Laws Governing NMR and MRI Material Properties NMR Signal Detection MRI Signal Detection or MRI Imaging MRI Construction T1, T2, and Proton Density Image MRI Modes or Pulse Sequence MRI Artifacts Light Microscopes Introduction Physical Principles Construction of a Wide-Field Microscope Epi-Illumination Fluorescence Microscope Confocal Microscopes Nipkow Disk Microscopes Confocal or Wide-Field? Electron Microscopes Introduction Physical Principles Construction of EM Specimen Preparations Construction of TEM Construction of SEM Appendix A: Installing Python Distributions Appendix B: Parallel Programming Using MPI4Py Appendix C: Introduction to ImageJ Appendix D: MATLAB and Numpy Functions Index A Summary and Exercises appear at the end of each chapter.