An Improved Binarisation Algorithm for Nailfold Capillary Skeleton Extraction
Nailfold capillaroscopy (NC) is a non-invasive imaging technique employed to assess the condition of blood capillaries in the nail fold, and is routinely used for the detection of scleroderma spectral disorders, Raynaud's phenomenon and other connective tissue diseases. While NC image evaluation is typically performed through manual inspection by an expert, computer-aided approaches of capillary inspection can reduce the time required for diagnosis. The aim of NC image analysis is usually to extract the skeletons of the capillaries present in the image which form the basis of further analysis for diagnosis. In this paper, we propose an improved binarisation technique for NC image analysis that addresses the challenge of non-uniform background by employing a Difference-of-Gaussian approach before thresholding, coupled with a post-processing stage to remove smaller image artefacts. Based on two previously published NC skeletonisation algorithms, we demonstrate that our technique leads to significantly better skeleton extraction on different types of NC images.