Quantitative Optical Imaging
Quantitative Optical Imaging explores the fundamentals of optical imaging in biology, especially molecular and cellular biology. Covered topics include an introduction to optics and microscopes, fluorescence microscopy and image analysis, and biological applications. MATLAB will be taught at the beginning of the course and used throughout for image processing. Prior experience with MATLAB (or Python) is highly recommended but not required.
This course is co-taught by Dr. Joe Dragavon and myself. It is available for students in Biochemistry and MCD Biology, as either an undergraduate course (BCHM/MCDB 4312) or a graduate course (BCHM/MCDB 5312). The graduate version requires students to host a journal club as the graduate-level requirement.
From Fall 2021 onwards, an additional one credit microscopy lab is offered for students enrolled in the course. However, note that space in the imaging facility is limited and approximately ten slots are available. Prior to this, the lab was only offered to graduate students completing the Interdisciplinary Quantitative Biology graduate program.
If you have questions about the course, please feel free to contact me.
Previous course materials
Note: The Fall 2020 course was taught virtually due to the COVID-19 pandemic.
Hosted on YouTube
- Part I: Introduction to MATLAB
- Part II: Analyzing still images
- Part III: Analyzing time-series images
- Lecture 2 - Introduction to MATLAB - Handout
- Lecture 4 - Matrices - Handout
- Lecture 6 - Performing calculations with matrices.pdf
- Lecture 8 - Debugging.pdf
- Lecture 12 - Digital Images.pdf
- Lecture 16 - Otsu’s method and Measuring cell properties.pdf
- Lecture 22 - Separating clusters of objects using the watershed algorithm.pdf
- Lecture 25 - Intensity corrections for quantitative imaging.pdf
- Lecture 27 - For loops, if statements, time lapse images.pdf
- Lecture 30 - Tracking moving objects using nearest-neighbor.pdf
- Lecture 32 - Tracking moving objects using nearest-neighbor (ii).pdf
- Lecture 34 - Visualizing tracking and 2D curve-fitting.pdf
- Curve-Fitting Review.pdf
- Lecture 1: MATLAB and matrices
- Lecture 2: matrix operations and scripts
- Lecture 3: Logical operations and images
- Lecture 4: Segmentation
- Lecture 5: Morphological operators
- Lecture 6: Watershed transform
- Lecture 7: Data types, mean and median filters
- Lecture 8: Plots and curve-fitting
- Lecture 9: Background subtraction, uneven illumination, ratiometric FRET
- Lecture 10: Analyzing videos and tracking bees
- Lecture 11: Particle localization and STORM
- MATLAB Reference Sheet