Introduction to Image Analysis

Graduate Course at the University of Bern

Welcome to the course website for Introduction to Image Analysis! This course covers concepts in digital image processing and analysis, with a focus on applications in biomedical engineering.

Course Overview

This graduate-level course provides a broad introduction to image analysis techniques and their applications in biomedical engineering. Topics include:

Image formation and representation
Image enhancement and filtering
Frequency domain processing
Image segmentation and feature extraction
Image registration and transformations
Machine learning and deep learning for image analysis
Medical image processing and 3D imaging

Course Information

Lectures: Wednesdays
Time: 13:15–15:00
Location: Hörsaal S481, Chemie, Biochemie und Pharmazie (DCBP)
Address: Freiestrasse 3, 3012 Bern
Assignment Due Time: 23:59
For course links and communication, see the Course Info page.

Schedule

Wed, Feb 18

Course Overview & Python for Image Analysis

Instructor: Amith Kamath

Assignment 0

Wed, Feb 25

Digital Image Formation & Representation

Instructor: Amith Kamath

Wed, Mar 4

Sampling, Quantization, Resolution

Instructor: Dr. Hugo Guillen Ramirez

Wed, Mar 11

Point Operations & Filtering

Instructor: Dr. Hugo Guillen Ramirez

Assignment 1

Wed, Mar 18

Edge Detection & Morphological Operations

Instructor: Dr. Hugo Guillen Ramirez

Wed, Mar 25

Image Segmentation

Instructor: Prof. Dr. Mauricio Reyes

Wed, Apr 1

Mid-term Exam (covers lectures 1-6)

Instructor: Prof. Dr. Mauricio Reyes

Wed, Apr 8

Feature Extraction

Instructor: Prof. Dr. Mauricio Reyes

Assignment 2

Wed, Apr 15

Image Registration & 2D vs. 3D Analysis

Instructor: Prof. Dr. Mauricio Reyes

Wed, Apr 22

Introduction to Deep Learning for Image Analysis

Instructor: Dr. Pablo Marquez Neila

Assignment 3

Wed, Apr 29

Generative Models & Advanced Architectures

Instructor: Dr. Pablo Marquez Neila

Wed, May 6

Best Practices, Reproducibility, and Medical Applications

Instructor: Amith Kamath

Wed, May 13

Flash Presentations of Demos (Assignment 2)

Instructor: Amith Kamath

Teaching Team

Amith Kamath (Instructor)
amith.kamath@unibe.ch

Dr. Hugo Guillen Ramirez (Instructor)
hugo.guillenramirez@unibe.ch

Prof. Dr. Mauricio Reyes (Instructor)
mauricio.reyes@unibe.ch

Dr. Pablo Marquez Neila (Instructor)
pablo.marquez@unibe.ch

Davide Scandella (Teaching Assistant)
davide.scandella@unibe.ch

Seyedeh Mirzagar (Teaching Assistant)
seyedeh.mirzargar@unibe.ch

Course Policies

Prerequisites

Students should have:

Basic programming experience (Python preferred)
Understanding of linear algebra and calculus
Familiarity with basic signal processing concepts

Grading

Assignments (40%): Four programming assignments (5%, 10%, 10%, 15%)
- All assignments are distributed and submitted via GitHub Classroom
- Assignment 0 includes Python environment setup and basic programming exercises
Mid-term Exam (30%): Covers classical image analysis (Lectures 1-6)
Final Exam (30%): Comprehensive exam covering all topics
Note: Higher final exam score can replace mid-term grade

Academic Integrity

All work submitted must be your own. Collaboration on assignments is permitted, but you must write your own code and acknowledge all collaborators. Plagiarism will not be tolerated.

Getting Started

Review the course information page
Check the schedule above for upcoming lectures and assignments
Set up your development environment with Python, NumPy, OpenCV, and scikit-image
See the assignments page to submit your work via GitHub Classroom

Contact

For course-related questions, please use ILIAS. For private matters, contact the instructors via email.

Acknowledgments

This course website is based on the design and structure of Stanford CS45: Software Tools Every Programmer Should Know, and The Missing Semester of Your CS Education (MIT CSAIL).

We are grateful to these educators for making their course materials and website designs openly available. The course content for Introduction to Image Analysis is original and developed by the University of Bern faculty.