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2007
Detection of malignant melanoma based on lesion images
Background
Computer-aided diagnostics (CAD) systems are already widely used in practical clinical situations for tasks such as the detection of breast and lung cancer and melanoma. Most of these systems are detection systems designed to aid physicians when looking for abnormalities in diagnostic data.
Goal
The main goal is to contribute to this research field by developing analysis methods that are suitable for fully automatic and scalable differential systems involving statistical inference for the purpose of diagnostic decision making. Since the goal of the project is to deliver the system to a wide population, the use of mobile devices will be investigated.
Problems
The challenges lie within automatic image segmentation that is robust and fast enough and in pattern recognition methods that can handle anatomical structures in images. Analysis methods also need to be tailored for images acquired by low-cost devices such as pocket-dermatoscopes and mobile phone digital cameras.
Methods
Existing segmentation techniques, Scale-space methods, and Gaussian Markov Random Fields will be applied. New robust methods suitable for this particular problem will be designed. Clinical trials to measure performance of the system.
Project members from partners
Prof. Fred Godtliebsen (UiT), Dr.Vedad Hadziavdic (NST)
Researchers
1 Post.doc researcher and 1 PhD student
International collaborators
Prof. Lasse Holmström, Department of Mathematics and Statistics, University of Oulo, Finland,
Dr. Jörg Polzehl, Weierstrass Institut für Angewandte Mathematik und Statistikk, Berlin, Germany
Prof. Probal Chaudhuri, Indian Statistical Institute, Calcutta, India
Prof. James Stephen Marron, University of North Carolina at Chapel Hill, Chapel Hill, USA.
Project start/stop
01.02.2007-31.01.2011
Contribution to health care
Diagnostic equipments that can be used by dermatologists in order to exploit resources in a more efficient way.
Contribution to new industry
Software that accompanies hardware products and that creates basis for new services.
Contact person at NST
PhD student Kevin Thon, direct telephone +47 977 85 755 and email Kevin.Thon@telemed.no
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