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Graduate courses

Departments' graduate courses for PhD-students.


Syllabus for

Academic year
SSY096 - Image analysis
Syllabus adopted 2014-02-13 by Head of Programme (or corresponding)
Owner: MPBME
7,5 Credits
Grading: TH - Five, Four, Three, Not passed
Education cycle: Second-cycle
Major subject: Bioengineering, Electrical Engineering

Teaching language: English
Open for exchange students
Block schedule: C

Course module   Credit distribution   Examination dates
Sp1 Sp2 Sp3 Sp4 Summer course No Sp
0114 Examination 7,5c Grading: TH   7,5c   21 Mar 2015 am V,  13 Apr 2015 pm V,  17 Aug 2015 pm M

In programs



Professor  Fredrik Kahl


ESS060   Image analysis SSY095   Image analysis

Course evaluation:

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In order to be eligible for a second cycle course the applicant needs to fulfil the general and specific entry requirements of the programme that owns the course. (If the second cycle course is owned by a first cycle programme, second cycle entry requirements apply.)
Exemption from the eligibility requirement: Applicants enrolled in a programme at Chalmers where the course is included in the study programme are exempted from fulfilling these requirements.

Course specific prerequisites

A basic course in Signals and Systems (or the equivalent) including the Fourier Transform, linear filter theory (impulse response, transfer function, convolution, sampling theorem). Working knowledge of probability theory.


The aim of this course is for students to gain a basic understanding of the methods and algorithms-including filtering, registration, segmentation, feature extraction, classification-for analysing and interpreting images and image sequences. The course seeks to integrate the theory presented in lectures with numerical exercises, practical demonstrations, and computer-based laboratory sessions that involve solving real-world image analysis problems ranging from industrial inspection to medical diagnosis.

Learning outcomes (after completion of the course the student should be able to)

- Explain and evaluate the characteristics of digital images (spatial resolution, contrast resolution, temporal resolution, colour resolution, spatial and temporal frequency properties, noise)
- Describe the duality between spatial and frequency domain descriptions of imaging data, apply Fourier Theory for transforming between the two, and apply linear filtering in both domains as a means of improving subjective image quality.
- Explain and apply state-of-the-art image analysis algorithms optimized for solving quantitative measurement problems.
- Motivate and apply image processing and analysis software tools such as the MATLAB Image Processing Toolbox for implementing and testing algorithms.
- Suggest and implement at least one major application including real-world images (Project). The application must be relevant to the student's major subject. For example, students enrolled in the bioengineering major should focus on applications relevant to the interpretation of clinical and cellular images such as the identification of biopolymers, subcellular structures, and cellular heterogeneity of tissues, and the localization of fluorescent biomolecules to subcellular structures.


The course covers the following:
1.Digital image fundamentals:
- Image representations, properties (resolution, frequency content, noise degradation), images as linear systems (Fourier transform, sampling, convolution), images as stochastic processes, linear and non-linear filtering, geometric transformations, and registration.
2.State-of-the-art image analysis and classification algorithms:
- Binary and grey-scale mathematical morphology (distance transform, morphological operators and filters, tophat transform).
- Representation and description of image objects: number of objects, size (contour and area descriptors), boundary (B-Splines, Dynamic Programming, Hough Transform), shape (Moments, Active Shape Contours, Active Shape Modelling), texture (Fourier based techniques, algorithms based on second-order statistics, autocorrelation), colour (colour spaces), and motion (optical flow).
- Image segmentation methods (thresholding methods, region-growing, watershed segmentation, mean-shift, level-sets, graph-cuts).
- Pattern recognition methods for region and object classification (Bayes classifier, Fisher's linear discriminant, k-nearest neighbour, Support Vector Machines, classifier design, evaluating classifier performance).
3. Applications:
- Colour image analysis in clinical odontology, lesion detection and characterization in breast MRI, brain segmentation for patient-specific modelling, quantitative microscopy.


The course is organised as a number of integrated lectures (including 3-4 guest lectures given by industry and academic researchers showcasing practical applications of image analysis), numerical exercises, and practical demonstrations. In addition there are four laboratory sessions and one project. The project may be carried out individually or in pairs. The project involves the submission of a written report explaining the image analysis problem at hand, a motivation of the chosen theory and algorithms, and results and conclusions.


Image Processing, Analysis and Machine Vision
By Sonka, Hlavac and Boyle
PWS Publishing, Pacific Grove


Written exam with TH grading, laboratory sessions (pass/fail), and project (pass/fail).

Page manager Published: Thu 04 Feb 2021.