Image and Data Analysis for Scientists

EN 580.128.13

MWF 2-5:45 pm

Course Instructors:

Sarah Somers

Smith 5001D

Ashley Farris

Smith 5001C

Room:

Recommended Textbook: Matlab Primer, 8th Edition by Timothy A. Davis

The goal of this course is to prepare students to utilize ImageJ and Matlab to analyze data obtained in an academic or research setting.

Learning objectives:

By the end of the course, students will be able to:

  • Learn what parameters constitute a “quality image” in a research setting
  • Know how to obtain a quality image on a bright field and fluorescence microscope
  • Open and manipulate images in ImageJ
  • Learn how to write and execute macros in ImageJ for large batches of images
  • Learn basics of Matlab programming language including plotting data, fitting data to curves, and basic statistical functions

Schedule:

Date / Topic / Assignment
1/8 / Intro to fluorescence/brightfield microscopy
What is a good image?
How do you acquire images?
ImageJ (Acquire Images)
  • Stacking
  • Channels
  • Macros/batch processing
/ Lab 1: ImageJ Macros for Batch Processing
1/10 / ImageJ
  • Segmentation
  • Intensity Analysis
  • Shape analysis
  • Removing imperfections/artifacts
/ Lab 2: Analyzing particles and vessels
1/12 / Continue from Day 2
Exporting data into Excel
Simple statistical analysis / Lab 3: Quantification of images from Lab 2
1/16* / Intro to Matlab
  • Open Data
  • Create plots
  • Make plots pretty
  • Standard Deviation/standard error
/ Lab 4: Matlab Introduction
1/17 / Matlab for solving differential equations
  • Plotting data and assessing the fit
  • Binding Kinetics Lab
/ Lab 5: Binding Kinetics Model
1/19 / Matlab
  • Smoothing data and taking out noise
  • Looking at minima and maxima
  • Setting the axes
/ Lab 6: Analyzing Muscle Contraction
1/22 / Matlab: Print quality assessments / Lab 7: Analyzing Image Accuracy
1/24 / Overflow/Project day
1/26 / Presentations

*Note that this class is held on Tuesday, to make up for the MLK holiday

Grading Scale:

This course will be graded as S/U, where ≥70% is satisfactory and <70% is unsatisfactory. Students are expected to attend all sessions and complete the lab assignments. A makeup day will be included in case students need more time to finish any particular lab assignment.

Grade Breakdown:

Attendance: 35%

Lab completion: 35%

Project Presentation: 15%

Project Write-up: 15%

Course Project:

The student will work in groups of 2-3 to develop a project that uses either an ImageJ macro or Matlab script to solve a research-related question of interest to the student. A rubric will be provided prior to submission.

Some project examples:

  • Writing a Matlab script to analyze data from the CDC and plot the incidence of measles in each state for the past 20 years
  • Write an ImageJ macro or Matlab program to count the number of nuclei or cells in a sample
  • Write a Matlab code to extract peaks from NMR data

The project submission will include commented code and a write-up (2-3 pages) that must include the following sections:

  • An introduction, describing the relevance of the problem and the need for the solution
  • An approach, describing the computational method used to address the problem
  • A description of functions used in the program to address the problem
  • A results section that includes a sample data set and the results obtained from the Matlab or ImageJ program
  • A discussion section describing limitations, future directions, and possible implications or impacts
  • A conclusion section summarizing the project

The presentation will be a 15-20 minute summary of the project. Presentations will follow the general format of the submission.