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