Single-Course Engelsk 5 ECTS

Image analysis

Overall Course Objectives

The objective of the course is to give an introduction to image analysis including topics within medical image analysis. In recent years, there has been an explosion in the use of image analysis for self-driving cars, advanced diagnostics, treatment planning, face tracking and a wide variety of other applications.
This course will give the student a solid understanding of digital images. Their nature, where they come from and which tools and techniques that can be used to extract information from them. After the course, the participant will be able to combine, implement , use and evaluate the performance of basic image analysis systems.

See course description in Danish

Explain the fundamental properties of digital images including medical images .
Describe principles for image acquisition including the pin hole camera and medical modalities like x-ray, computed tomography and magnetic resonance imaging.
Implement and use basic image coding for storing images.
Implement and apply image analysis algorithms, including point processing, filtering, morphology, BLOB analysis, pixel classification, geometrical transforms, registration, classification, boundary tracing, line detection and segmentation.
Implement and use statistical methods like principal component analysis for processing images and extraction of significant information from images.
Implement and use classification algorithms based on extracted image features.
Select appropriate training data and do feature selection for image based classification algorithms.
Implement and use statistical shape analysis algorithms.
Design an appropriate image analysis algorithm based on a set of example images and a given objective.
Compute the performance of a given image analysis algorithm given a ground-truth data set.
Improve and optimize image algorithms based on ground truth data or clinical end-points.

Digital image and data structures, the pin hole camera, medical modalities, image file formats, point processing, filtering, morphology, BLOB analysis, classification, labelling and object analysis, pixel classification, segmentation, geometrical transforms, registration, boundary tracing, line detection, shape analysis, confusion matrices, and performance metrics.

01001/01002/01003/01004/01005/01006/01015/01016/02101/02102/02402/02403/02405/02002/02631/02632/02633/02692, Basic knowledge of probability theory or statistics. Basic course in linear algebra. Basic programming experience in for example Matlab, Python, C, C#, C++ or Java.

Lectures and computer exercises.