Demonstrator project: Apparatus for skin imaging and skin image representation

Construction of a vision-based system for acquisition of dermatological images. The system integrates light box, lighting, sensor, computer, and software. Standardized lighting conditions, color and geometric calibration provides the standardized images that are required for comparative studies in distributed databases. A carefully designed human-computer interface will make the system operational for non-specialists. A larger scale system for industrial surfaces has already been developed in the "Vision-based measurements" project at IMM sponsored by the Danish Center for IT-technology. The system should be constructed and optimized for dermatological images, and this includes considerations on appropriate lighting, wavelengths, sensors, and algorithms. Besides storing standardized images the system should store summarizations of the images based on visual cues like color, texture, and size and shape of specific features. The group has an extensive experience on this from the EU ESPRIT project CATIE (Color and Texture Inspection Equipment) that has been running since early 1996.

Diagnosis support systems

Malignant Melanoma is the most lethal of skin cancers. The neural network group has worked with the Department of Plastic Surgery, Rigshospitalet, for several years on a pattern recognition system for diagnosis support based on skin microscopy. Based on the advanced skin imaging systems mentioned above and implementation of more robust color measures and a richer set of features we will develop a demonstration system for visual detection of Malignant Melanoma. A promising collaboration with the Department of Dermatology at Bispebjerg Hospital, based on skin sample (and possibly later in vivo) Raman Spectroscopy will add new possibilities for diagnosis support in this important application area.

Datamining in distributed medical databases

In line with the definition of telemedicine given above these systems serve dual purposes: they can assist medical research and in the clinic medical diagnosis for medical professionals and secondly, they can be used as information sources for patients. Fact finding in distributed medical databases by use of advanced machine learning methods can be used to extract important information for the medical scientist. Furthermore, such visual datamining can be used to search for image analogies for patients directly on the internet, say by search for similar images of rashes, allergies or other skin lesions. Visualization of high-dimensional datasets is very important for understanding complex distributed databases. Visualization and hypertext tools are concurrently being developed for the field of neuroimaging within a THOR project granted in the field of neuroinformatics. Similar ideas are pursued by the international organization "Internet Dermatology Society''

- Link to Internet Dermatology Society

Novelty detection in video and image signals

Common to several medical imaging problems is the need for tracking change over time in image sequences, video footage or other measurements. Modern statistical techniques such as Bayesian density modeling can be used to model normal behavior, hence used to detect abnormal behavior and significant change. Dermatological applications are multiple within monitoring of skin lesion and wound healing. The Image Analysis Group has extensive experience in estimation of optical flow. If a large number of consecutive and well registered images can be taken of the area of interest estimation of image flow will result in the estimation of whether a lesion "grows'' or "shrinks'' and the speed growth or shrinkage. Another possible scheme is inspired by a multivariate statistical technique called canonical correlations. Here the collection of images over time is analyzed in order to find changes in a multivariate fashion. Earlier work has analyzed both a bitemporal model the truly multitemporal problem.