Computerised image analysis is about developing computational methods for extracting meaningful information from images - mainly from digital images - by means of digital image processing techniques, including for example convolutional neural networks (CNNs).
We develop theory, methods, algorithms, and systems to address questions related to life science, medicine, digital humanities, and other applications. This include identifying objects, extracting measurements, and making decisions based on image data. Many methods are common for wide ranges of applications. The list of research topics below is therefore related to several of our research entities and projects.
Over the years, more and more of the research in image analysis involves development and application of model- and learning-based (AI) methods. However, some traditional methods also necessarily remain and are developed.
- Image reconstruction and de-noising includes methods for improving the quality of image data.
- Image registration addresses methods to computationally align image data collected e.g. at different time points or with different imaging modalities.
- Digital geometry focuses on deriving geometric information from digital images, taking the limitations of discrete representations into account.
- Object detection can broadly include both delineation and classification of objects and images.
- Feature extraction includes approaches to extracting measurements and other properties from objects or regions of interest in images, relevant for the subsequent analysis.
- Image understanding is the ultimate goal of image processing and analysis, and provides interpretation of the information contained in the image data.
- Visualization is any technique for creating images, diagrams, or animations to communicate a message - make the invisible of scientific data visible.
- End-to-end image analysis proposes deep learning-based methods optimized to directly interpret image data fed into the system, without performing any intermediate steps.
- Centre for Image Analysis
- BioImage Informatics Facility
- Hand Written Text Recognition
- Medical Image Processing
- MIDA - Methods for Image Data Analysis
- Quantitative Microscopy
- CAiM - Computer-assisted Applications in Medicine
- TissUUmaps project
- HASTE project
- q2b – From Quill to Bytes
- Haptics and Visualization in Medicine
- Amin Allalou
- Christophe Avenel
- Ewert Bengtsson
- Gunilla Borgefors
- Orcun Göksel (see also his homepage)
- Anders Hast (see also his homepage)
- Christer Kiselman
- Anna Klemm
- Joakim Lindblad (see also his homepage)
- Filip Malmberg
- Fredrik Nysjö (see also his homepage)
- Ingela Nyström (see also her homepage)
- Petter Ranefall
- Stefan Seipel
- Ida-Maria Sintorn
- Nataša Sladoje (see also her homepage)
- Robin Strand (see also his homepage)
- Carolina Wählby (see also her homepage)
- 1st Prize of the AI Sweden and AstraZeneca Adipocyte Cell Imaging Challenge to the HASTE team
- Master's programme in Image Analysis and Machine Learning.
- 1MD110 Introduction to Image Analysis
- 1MD120 Deep Learning for Image Analysis
- 1MD130 Digital Imaging Systems
- 1MD037 Advanced Image Analysis
- 1TD396 Computer-Assisted Image Analysis I
- 1MD140 Scientific Visualization
- 1MD150 Computer Graphics
- 1MD026 and 1MD030 Medical Informatics
- 1MD036 Project in Software Development in Image Analysis and Machine Learning
- 1MD038 Degree Project E in Image Analysis and Machine Learning