Medical image segmentation aims to detect the boundaries separating different objects from the background inside a two-dimensional or three-dimensional medical image. Medical images are highly variable in nature, and this makes the medical image segmentation difficult. The variations arise because of major modes of variation in human anatomy and because of different modalities of the images being segmented (for example, X-ray, MRI, CT, microscopy, endoscopy, OCT, and so on) used to obtain medical images. Further diagnostic insights can be obtained from segmentation results to help doctors make decisions. Regions with missing edges, the absence of texture contrast, and so on create major issues, and many segmentation approaches have been proposed to fix them. The automatic measurement of organs, cell counting, and simulations based on the extracted boundary...
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Python Image Processing Cookbook
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Python Image Processing Cookbook
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Overview of this book
With the advancements in wireless devices and mobile technology, there's increasing demand for people with digital image processing skills in order to extract useful information from the ever-growing volume of images. This book provides comprehensive coverage of the relevant tools and algorithms, and guides you through analysis and visualization for image processing.
With the help of over 60 cutting-edge recipes, you'll address common challenges in image processing and learn how to perform complex tasks such as object detection, image segmentation, and image reconstruction using large hybrid datasets. Dedicated sections will also take you through implementing various image enhancement and image restoration techniques, such as cartooning, gradient blending, and sparse dictionary learning. As you advance, you'll get to grips with face morphing and image segmentation techniques. With an emphasis on practical solutions, this book will help you apply deep learning techniques such as transfer learning and fine-tuning to solve real-world problems.
By the end of this book, you'll be proficient in utilizing the capabilities of the Python ecosystem to implement various image processing techniques effectively.
Table of Contents (11 chapters)
Preface
Image Manipulation and Transformation
Free Chapter
Image Enhancement
Image Restoration
Binary Image Processing
Image Registration
Image Segmentation
Image Classification
Object Detection in Images
Face Recognition, Image Captioning, and More
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