Image Processing and Acquisition using Python, Second Edition provides readers with a sound foundation in both image acquisition and image processing—one of the first books to integrate these topics. 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 second 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.
New to this edition
I Introduction to Images and Computing using Python. 1. Introduction to Python. 2. Computing using Python Modules. 3. Image and its Properties. II Image Processing using Python. 5. Image Enhancement. 6. Affine transformation. 7. Fourier Transform. 8 Segmentation. 9 Morphological Operations. 10. Image Measurements. 11. Neural network. 12. Convolutional neural network. III Image Acquisition. 13. X-Ray and Computed Tomography. 14. Magnetic Resonance Imaging. 15. Light Microscopes. 16. Electron Microscopes. Appendices.
Python has been ranked as the most popular programming language, and it is widely used in education and industry. This book series will offer a wide range of books on Python for students and professionals. Titles in the series will help users learn the language at an introductory and advanced level, and explore its many applications in data science, AI, and machine learning. Series titles can also be supplemented with Jupyter notebooks.
Please contact us if you have an idea for a book for the series.