UQ-bio Summer School

0.5 – Basic Image Loading and Processing Matlab Tutorial (Dr. Brian Munsky)

Lecture 0.5

Title: Tutorial — Basic Image Loading and Manipulation in Matlab

Lecturer: Prof. Brian Munsky

Lecturer Website: https://www.engr.colostate.edu/~munsky/

Lecturer Email: brian.munsky@colostate.edu

Learning Objectives:

      • Load tif images in Matlab

      • Perform basic tensor operations on tiff images in Matlab

Dr. Brian Munsky joined the Department of Chemical and Biological Engineering and the School of Biomedical Engineering as an assistant professor in January of 2014. He received B.S. and M.S. degrees in Aerospace Engineering from the Pennsylvania State University in 2000 and 2002, respectively, and his Ph.D. in Mechanical Engineering from the University of California at Santa Barbara in 2008. Following his graduate studies, Dr. Munsky worked at the Los Alamos National Laboratory — as a Director’s Postdoctoral Fellow (2008-2010), as a Richard P. Feynman Distinguished Postdoctoral Fellow in Theory and Computing (2010-2013), and as a Staff Scientist (2013). Dr. Munsky is best known for his discovery of Finite State Projection algorithm, which has enabled the efficient study of probability distribution dynamics for stochastic gene regulatory networks. Dr. Munsky’s research interests at CSU are in the integration of stochastic models with single-cell experiments to identify predictive models of gene regulatory systems, and his research is actively funded by the W M Keck Foundation, the NIGMS (MIRA), and the NSF (CAREER). Dr. Munsky is very excited about the future of quantitative biology, and he would love to talk about this with you!

Title: Basic Image Loading and Manipulation in Matlab

Abstract: abc abc abc

Suggested Reading or Key Publications:

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Links to Relevant Software: 

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    •  Make an image showing just the red channel at the 23 time point of the tensor video.
    • Make a smaller image that is compressed to 1/2 resolution in X, and 1/3 resolution in Y.
    • Make a color image (3 channels) where you enhance the blue channel intensity by a value
    • Create a cropped video image tensor, which is centered around the brightest pixel of the image (in each individual frame) and is a 51×51 cutout of the original video/tensor. Do this before and after applying a gaussian filter.