Hunter Elliott, PhD
Image and Data Analysis Core
Often acquiring fluorescence images is only the first step in answering your biological question: The images contain the information you're interested in, but now how can you extract it? We will start with the basic concepts necessary for understanding and utilizing images as data, and then survey the most commonly applied image analysis methods. Topics include segmentation, filtering, co-localization, particle detection and tracking, super-resolution methods and more. No computational or mathematical background is required, and all topics will be illustrated with easy-to-understand examples using real data.