Perfusion data are dynamic medical image data which characterize the regional blood flow in tissue. We focus on the visualization and exploration of perfusion data for diagnostic purposes in three major application areas: ischemic stroke diagnosis, breast tumor diagnosis and the diagnosis of coronary heart disease.
For a reliable diagnosis, the multi-dimensional space of perfusion parameters needs to be explored. Therefore, we aim at different exploration and visualization techniques for an improved diagnosis. Furthermore, visual analytics approaches have been developed and integrated in the diagnosis frameworks.
During the evaluation of perfusion data, clustering is often carried out to reduce the influence of noise or image artifacts when analyzing the temporal variation of the tissue’s signal intensity values. Thus, the exploration of perfusion data comprises the adaption of clustering methods as well as the evaluation of these clustering methods based on an automatic classification into benign and malignant tumors.
This project was a subproject of the DFG funded Scalable Visual Analytics priority programme.
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