The Computer Vision and Image Processing (CVIP) group carries out research on biomedical image analysis, computer vision, and applied machine learning. We have forged a portfolio of interdisciplinary collaborations to bring advanced image analysis technologies into a range of medical, healthcare and life sciences applications. Our computer vision research on understanding of human activities has a range of applications that includes situated support systems, rehabilitation, and security. CVIP teams have won several international challenge contests:

WMH Segmentation Challenge at MICCAI 2017

A team from CVIP placed first in this Grand Challenge held at MICCAI 2017 in Quebec. The challenge compared methods for automatic segmentation of White Matter Hyperintensities (WMH) of presumed vascular origin in brain MR images.

Endoscopic Vision Challenge at MICCAI 2015

Software submitted by CVIP achieved first place in contests on early Barrett’s cancer detection (Barrett’s Cancer Detection Award) and on detection of abnormalities in gastroscopic images (Polyp Localization Award) at the MICCAI 2015 Endoscopic Vision Challenge held in Munich.

I3A Contest at ICPR 2014

Software from CVIP achieved first place in both of the tasks (cell classification and specimen classification) in the Contest on Performance Evaluation of Indirect Immunofluorescence Image Analysis Systems (I3A), held at ICPR 2014 in Stockholm. You can read about the winning methods in our Pattern Recognition paper.

New VAMPIRE collaborations
The Dundee-Edinburgh VAMPIRE team is delighted to announce new collaborations with Duke University (Dr Sharon Fekrat) and NIDEK Technologies, Italy
MICCAI Workshops
CVIP will co-organise 4 workshops at MICCAI 2018 in Granada: COMPAY, PRIME, CNI, and OMIA (twinned with the REFUGE challenge)
Royal Society meeting
Manuel Trucco is chairing a SciencePlus meeting at the Royal Society, London (23-24 April) on The Transformative Potential of Data and Image Analysis for Eye Care
VAMPIRE in £7m NIHR project
VAMPIRE/CVIP will deliver measurements from up to 20,000 retinal images within an India-Scotland project on personalized medicine for diabetes as part of the NIHR Global Health funding programme