Vascular image registration techniques: A living review.

Registration of vascular structures is crucial for preoperative planning, intraoperative navigation, and follow-up assessment. Typical applications include, but are not limited to, Trans-catheter Aortic Valve Implantation and monitoring of tumor vasculature or aneurysm growth. In order to achieve the aforementioned goals, a large number of various registration algorithms has been developed. With this review paper we provide a comprehensive overview over the plethora of existing techniques with a particular focus on the suitable classification criteria such as the involved modalities of the employed optimization methods. However, we wish to go beyond a static literature review which is naturally doomed to be outdated after a certain period of time due to the research progress. We augment this review paper with an extendable and interactive database in order to obtain a living review whose currency goes beyond the one of a printed paper. All papers in this database are labeled with one or multiple tags according to 13 carefully defined categories. The classification of all entries can then be visualized as one or multiple trees which are presented via a web-based interactive app (http://livingreview.in.tum.de) allowing the user to choose a unique perspective for literature review. In addition, the user can search the underlying database for specific tags or publications related to vessel registration. Many applications of this framework are conceivable, including the use for getting a general overview on the topic or the utilization by physicians for deciding about the best-suited algorithm for a specific application.

Illumination Compensation and Normalization Using Low-Rank Decomposition of Multispectral Images in Dermatology.

When attempting to recover the surface color from an image, modelling the illumination contribution per-pixel is essential. In this work we present a novel approach for illumination compensation using multispectral image data. This is done by means of a low-rank decomposition of representative spectral bands with prior knowledge of the reflectance spectra of the imaged surface. Experimental results on synthetic data, as well as on images of real lesions acquired at the university clinic, show that the proposed method significantly improves the contrast between the lesion and the background.