Buch, Englisch, 390 Seiten, Format (B × H): 159 mm x 241 mm, Gewicht: 690 g
Buch, Englisch, 390 Seiten, Format (B × H): 159 mm x 241 mm, Gewicht: 690 g
Reihe: Chapman & Hall/CRC the Python
ISBN: 978-1-4665-8375-7
Verlag: CRC PR INC
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.
Autoren/Hrsg.
Weitere Infos & Material
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 PropertiesImage TypesData Structures for Image AnalysisProgramming Paradigm
Image Processing using Python Spatial Filters Introduction FilteringEdge Detection using Derivatives
Image Enhancement Introduction Pixel Transformation Image Inverse Power Law Transformation Log Transformation Histogram EqualizationContrast Stretching
Fourier Transform Introduction Definition of Fourier Transform Two-Dimensional Fourier Transform Convolution Filtering in Frequency Domain
Segmentation Introduction Histogram-Based SegmentationRegion-Based SegmentationSegmentation 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 IntroductionLabeling Hough TransformTemplate 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 MRIMaterial PropertiesNMR Signal Detection MRI Signal Detection or MRI ImagingMRI ConstructionT1, T2, and Proton Density Image MRI Modes or Pulse SequenceMRI Artifacts
Light Microscopes Introduction Physical PrinciplesConstruction of a Wide-Field Microscope Epi-Illumination Fluorescence MicroscopeConfocal Microscopes Nipkow Disk MicroscopesConfocal or Wide-Field?
Electron Microscopes Introduction Physical PrinciplesConstruction of EMSpecimen Preparations Construction of TEM Construction of SEM
Appendix A: Installing Python DistributionsAppendix B: Parallel Programming Using MPI4PyAppendix C: Introduction to ImageJ Appendix D: MATLAB and Numpy Functions
Index
A Summary and Exercises appear at the end of each chapter.
