MD-LBPV Texture Analysis for Nailfold Capillaroscopy Image Classification

Nailfold capillaroscopy (NC) is a non-invasive imaging technique employed to assess the condition of blood capillaries in the nailfold and is particularly useful for diagnosis of scleroderma spectrum disorders and Raynaud’s phenomenon. Diagnosis is based on the identification of particular scleroderma patterns in the images which are typically grouped into early, active and late patterns. In this paper, we present a computer vision approach to recognising scleroderma patterns in NC images. Following a preprocessing step to enhance image quality, we extract texture information in a holistic way rather than trying to extract and measure individual capillaries. As texture features we employ multi-dimensional LPB variance descriptors which capture multi-resolution texture and local contrast information. Our experimental results confirm our approach to work well and to outperform an earlier approach.