Here are the lectures, exercises, and additional course materials corresponding to the spring semester 2015 course at ETH Zurich, 227-0966-00L: Quantitative Big Imaging. The lectures have been prepared and given by Kevin Mader, Anders Kaestner, and Marco Stampanoni. Please note the Lecture Slides and PDF do not contain source code, this is only available in the handout file.
- 19th February - Introduction and Workflows
- Lecture Slides
- Lecture Handout
- 26th February - Image Enhancement (A. Kaestner)
- Lecture Slides
- 5th March - Basic Segmentation, Discrete Binary Structures
- Lecture Slides
- Lecture Handout as PDF
- 12th March - Advanced Segmentation
- Lecture Slides
- Lecture Handout as PDF
- 19th March - Applications Graphical Models and Machine Learning (A. Lucchi)
- Lecture Slides
- 26th March - Analyzing Single Objects
- Lecture Slides
- Lecture Handout as PDF
- 2nd April - Analyzing Complex Objects
- Lecture Slides
- Lecture Handout
- 16th April - Spatial Distribution
- Lecture Slides
- Lecture Handout as PDF
- 23rd April - Statistics and Reproducibility
- Lecture Slides
- Lecture Handout as PDF
- 30th April - Dynamic Experiments
- Lecture Slides
- Lecture Handout as PDF
- 7th May - Scaling Up / Big Data
- Lecture Slides
- Lecture Handout as PDF
- 21th May - Guest Lecture - Applying Image Processing
- Image registration: a case study on material science - A. Patera: EMPA and Paul Scherrer Institut
- Introduction to High Content Screening - M. Prummer: NEXUS Personalized Medicine Center (formerly Roche)
- Application of High Content Screening - S. Noerrelykke: ScopeM
- 28th May - Project Presentations
- Bacteria-Hyphae interaction in microfluidic channels - Benedict Borer
- Multi-contrast X-ray imaging-based study of aggregates in cement-based mortars - Fei Yang
- Mapping Non/Less-Porliferative Cells in the Adult Zebrafish Tissue - Hanyu Qin
- Dynamic tracking of lithium volume in a lithium-ion battery, using synchrotron X-ray tomographic microscopy - Patrick Pietsch
- Equilibrium Catalyst - Rosh Jacob
- Coalescence kinetics of emulsions - Natalie Scheuble
- Microcalcifications - Federica Fraschetti, Vittoria Storni, Matthias Dzung
The exercises are based on the lectures and take place in the same room after the lecture completes. The exercises are designed to offer a tiered level of understanding based on the background of the student. We will (for most lectures) take advantage of an open-source tool called KNIME (www.knime.org), with example workflows here (https://www.knime.org/example-workflows). The basic exercises will require adding blocks in a workflow and adjusting parameters, while more advanced students will be able to write their own snippets, blocks or plugins to accomplish more complex tasks easily. The exercises from last year (available on: kmader.github.io/Quantitative-Big-Imaging-Course/) are done entirely in ImageJ and Matlab for students who would prefer to stay in those environments (not recommended)
The exercises will be supported by Filippo Arcadu, Kevin Mader, and Christian Dietz. There will be office hours in ETZ H75 on Thursdays between 14-15 or by appointment.
- 19th February - Introduction and Workflows (Christian Dietz, Intro to KNIME for Image Processing)
- Setup
- Workflow and Data
- 26th February - Image Enhancement (A. Kaestner)
- KNIME Exercises
- Matlab Exercises (for students experienced in Matlab)
- Starting Data / Matlab Directory
- 5th March - Basic Segmentation, Discrete Binary Structures
- Exercise Slides
- Workflows
- 12th March - Advanced Segmentation and Processing
- KNIME Exercises
- Old Lecture Quiz
- 19th March - Machine Learning in Image Processing (A. Lucchi)
- KNIME Exercises
- 26th March - Analyzing Single Objects
- KNIME Exercises
- 2nd April - Analyzing Complex Objects
- KNIME Exercises
- 16th April - Spatial Distribution
- KNIME Exercises
- 23rd April - Statistics and Reproducibility
- KNIME Exercises
- 30th April - Dynamic Experiments
- KNIME Exercises
- Old Exercise Slides
- Old Test Images
- 7th May - Scaling Up / Big Data
- KNIME Exercises
- Spark Data
- 21th May - Guest Lecture - Applying Image Processing
- Old Exercise Slides
- 28th May - Project Presentations
- Create an issue (on the group site that everyone can see and respond to, requires a Github account), issues from last year
- Provide anonymous feedback on the course here
- Or send direct email (slightly less anonymous feedback) to Kevin
The final examination (as originally stated in the course material) will be a 30 minute oral exam covering the material of the course and its applications to real systems. For students who present a project, they will have the option to use their project for some of the real systems related questions (provided they have sent their slides to Kevin after the presentation and bring a printed out copy to the exam including several image slices if not already in the slides). The exam will cover all the lecture material from Image Enhancement to Scaling Up (the guest lecture will not be covered). Several example questions (not exhaustive) have been collected which might be helpful for preparation.
- Project Signup
- Here you signup for your project with team members and a short title and description
- List
- Course Wiki (For Questions and Answers, discussions etc)
- Main Page
- Performance Computing Courses
- High Performance Computing for Science and Engineering (HPCSE) I
- Introduction to GPU Programming
- Programming Massively Parallel Processors with CUDA
- Reprodudible Research Courses
- Course and Tools in R
- Coursera Course