Research

The overall purpose of systems developed in the field of medical informatics is to extract and present clinically relevant information. Medical information appears in many different forms: Text, Parameters,Value Measurements, Time Courses, Images, Volumes, Image and Volume Sequences. Methods to attain new types of information is continuously being developed and the detail and quality of recorded data is increasing rapidly. Huge amounts of potentially relevant information can be tied to one single patient. In addition it is necessary to be able to integrate and analyze information from a large number of patients and time instances.

This development has led to a situation where we are at risk of drowning in the overwhelming flow of information. To be able to extract the relations that are pertinent in a given situation and to present them in a way that is simple to understand is rapidly becoming the main problem. Efficient solutions of this problem will be crucial components in future health care. To develop principles and methods for such solutions is the goal the research in the field of medical informatics. In particular the research group focuses on the development of systems for medical decision support and medical image analysis.

Ongoing research projects 

3D-4D Medical Image Enhancement	VR, Context Vision
Advanced Home Healthcare Environment	Vinnova, SITI, ITHS2
Antibiotic Resistance in ICUs
Data to Knowledge in Oncology	FORSS
Functional Magnetic Resonance Imaging	VRm
Hip Fracture Surgery Simulator	Vinnova, Melerit
Similar - The European taskforce creating human-machine interfaces SIMILAR to human-human communication.	EU NoE 'SIMILAR'
Semantic Interoperability	VR
SemanticMining	EU NoE
The Classification Browser
Tensor Signal Processing	NiMed, LiU
Risk Factor Analysis
Reduction of Motion Artifacts in MRI	VRnt
Automated Hippocampus Segmentation	VR
Manifold-Valued Signal Processing	VR, LiU

Past research projects 

Filter networks	Vinnova, Context Vision
Analysis of ultrasound images	Amersham, Vinnova, NiMed
Digital Mammography	MAMEA, Vinnova, NiMed
Analys av MR-bilder med ANN
GALEN/GALEN-IN-USE
Spriterm
TEMEC
Morphological Angiography	Vinnova
Learning in High Dimensional Signal Spaces	VRnt
HELIOS
KUSIVAR
PT-pol
Vision as Process (VAP)	EU Research Project
Stem Cell Motion Analysis	SSF
Image Sequence Quality Estimation	SSF