The effects of systemic sclerosis (SSc) - a disease of the connective tissue causing blood flow problems that can require amputation of the fingers - can be observed indirectly by imaging the capillaries at the nailfold, though taking quantitative measures such as blood flow to diagnose the disease and monitor its progression is not easy. Optical flow algorithms may be applied, though without ground truth (i.e. known blood flow) it is hard to evaluate their accuracy. We propose an image model that generates realistic capillaroscopy videos with known flow, and use this model to quantify the effect of flow rate, cell density and contrast (among others) on estimated flow. This resource will help researchers to design systems that are robust under real-world conditions. © 2013 IEEE.
|Number of pages||3|
|Journal||Annual International Conference of the IEEE Engineering in Medicine and Biology. Proceedings|
|Publication status||Published - 2013|