Computer Vision for Microscopy Image Analysis
High-throughput microscopy enables researchers to acquire thousands of images automatically over a short time, making it possible to conduct large-scale, image-based experiments for biological or biomedical discovery. However, visual analysis of large-scale image data is a daunting task. The post-acquisition component of high-throughput microscopy experiments calls for effective and efficient computer vision techniques.
Computer Vision for Microscopy Image Analysis provides a comprehensive and in-depth introduction to state-of-the-art computer vision techniques for microscopy image analysis, demonstrating how they can be effectively applied to biological and medical data.
The reader of the book will learn:
- How computer vision analysis can automate and enhance human assessment of microscopy images for discovery
- The important steps in microscopy image analysis
- State-of-the-art methods for microscopy image analysis including machine learning and deep neural network approaches
This reference on the state-of-the-art computer vision methods in microscopy image analysis is suitable for researchers and graduate students interested in analyzing microscopy images or for developing toolsets for general biomedical image analysis applications.
- Each topic contains a comprehensive overview of the field, followed by in-depth presentation of a state-of-the-art approach
- Perspectives and content contributed by both technologists and biologists
- Tackles specific problems of detection, segmentation, classification, tracking, cellular event detection
- Contains the fundamentals of object measurement in microscopy images
- Contains open source data and toolsets for microscopy image analysis on an accompanying website