# TEACHING

## Quantitative Optical Imaging

### Course description

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.

### Additional details

This course is co-taught by 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 present a journal club.

If you have questions about the course, please feel free to contact me.

### Previous course materials

I teach the image analysis portion of the course. Some of the materials from my previous lectures are listed here.

Some recordings are also available on YouTube:

##### Lecture handouts

- Lecture 3 - Getting started with MATLAB
- Lecture 4 - Working with matrices
- Lecture 6 - Importing and displaying images
- Lecture 8 - Performing calculations with matrices
- Lecture 11 - Correcting uneven illumination and debugging code
- Lecture 16 - Intensity thresholding
- Lecture 18 - Intensity histograms and Otsu’s method
- Lecture 20 - Bradley’s method and morphological operations
- Lecture 23 - The watershed algorithm
- Lecture 26 - Logical indexing and for loops
- Lecture 29 - Image analysis tips
- Lecture 30 - Multichannel and time-lapse images
- Lecture 33 - Tacking objects in a movie
- Lecture 38 - Data analysis tips

##### Lab handouts

- First time imaging guide
- Lab 1 - Measuring the FWHM of a diffraction-limited bead
- Lab 2 - Illumination quality, detector sensitivity, and refractive index mismatch
- Lab 3 - Tracking cells in time-lapse microscope images
- Lab 4 (Part I) - Training an image classification network
- Lab 4 (Part II)- Evaluating a classification network

Note: The Fall 2020 course was taught virtually due to the COVID-19 pandemic.

##### Recorded lectures

Hosted on YouTube

- Part I: Introduction to MATLAB
- Part II: Analyzing still images
- Part III: Analyzing time-series images

##### Lecture handouts

- 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 handouts

- 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