Bioimage informatics 2
– Image processing and analysis (5 ECTS)
Universtity of Turku

Entry requirements and/or target group: Fulfilling the requirements for university student, Passed the course Bioimage Informatics 1 or similar knowledge. Target group are master students (biosciences, biomedicine etc.), PhD students, Bachelor students (biotechnology), all Finnish Universities

Learning outcomes:

After the course students:

  • Know how to visualize, animate, process and quantitatively analyze multi-dimensional digital bioimages.
  • Understands the ethics and are able to prepare images for publication.
  • Are able to produce and evaluate analysis from image data.
  • Are able to apply more advanced image analysis techniques focusing on automated processes, and handling multi-dimensional images.

Content
This course covers the following topics:
1. Advanced edge detection / distance transforms

2. Advanced segmentation (machine learning/clustering/... )

3. Motion tracking and multi-dimensional image analysis

4. Deconvolution and fourier-based analysis

5. Batch processing

7. ROIs and object classification

8. Colocalization

9. Troubleshooting

10. Ethics and preparing images for publication

11. Introduction to automatic image analysis (using basic coding/macros or python or similar…)

Learning methods                   

Online material (text + videos) including examples and tutorials, quizzes, assignments, online discussions

Teaching language: English

Evaluation and evaluation criteria:                

Passed/failed. Approved on-line exercises and group work.

Timetable: 5-8 weeks, Spring 2020


Bioimage informatics 1
– Basic image processing and analysis (5 ECTS)

Universtity of Turku

In this course you will learn the basics of image processing and analysis in the point of view of life scientist.

Entry requirements and/or target group: Target group are master students (biosciences, biomedicine etc.), PhD students, bachelor students (biotechnology), all Finnish universities

Learning outcomes:
The goal is to learn basics of image processing and analysis in the point of view of life scientist.After the course students:

  • Know and understand the fundamental concepts in image processing and analysis
  • Understand how image processing and analysis can be used in science
  • Are able to do simple basic image processing tasks
  • Are able to produce and evaluate reliable data analysis from images

Content:
This course covers the following topics:
1. Digital Images

2. Dimensions

3. General image handling

4. Pixel processing

5. Neighborhood operations

6. Basic filters

7.  Edge detection

8. Thresholding

9. Basic morphological operations

10. Basic segmentation and counting objects

11. Transforms

12. Introduction to metadata in images

13. Introduction to Ethics

14. Introduction to machine learning

Learning methods:
Online material (text + videos) including examples and tutorials, quizzes, assignments       

Teaching language: English

Evaluation and evaluation criteria: Grade 0-5. Approved on-line exercises and assignments.

Timetable: Autumn 2019, 5-8 weeks