An Evaluation of Image Enhancement Techniques for Nailfold Capillary Skeletonisation

Nailfold capillaroscopy (NC) is a routine technique used to assess the characteristics and morphology of nailfold capillaries. Observation of micro-blood vessels in the nailfold is important for diagnosing diseases that lead to morphological changes of capillaries such as scleroderma, Raynaud's phenomenon and other connective tissue diseases. In order to support a computer-aided diagnosis approach to analysing NC images, several approaches have been proposed in the literature aiming to extract capillaries. In general, such capillary skeletonisation algorithms involve an image pre-processing step, followed by binarisation and finally extraction and definition of the capillary skeletons. Since image denoising and enhancement in the pre-processing step can have a major impact on the subsequent analysis, in this paper, we evaluate the performance of five enhancement techniques for the purpose for nailfold capillary skeletonisation. In particular, we investigate the α-trimmed filter, bilateral filter, bilateral enhancer, anisotropic diffusion filter and non-local means and integrate them with three capillary extraction algorithms from the literature. We report visual and quantitative performance on a set of diverse NC images. The obtained results indicate that a relatively simple α-trimmed filter, combined with a skeletonisation algorithm incorporating a difference-of-Gaussian approach to address non-uniform lighting and an iterative rule-based skeletonisation procedure, leads to the best results when comparing the obtained skeletonisations to a manually obtained ground truth

Automated Stabilization, Enhancement and Capillaries Segmentation in Videocapillaroscopy

Oral capillaroscopy is a critical and non-invasive technique used to evaluate microcirculation. Its ability to observe small vessels in vivo has generated significant interest in the field. Capillaroscopy serves as an essential tool for diagnosing and prognosing various pathologies, with anatomic–pathological lesions playing a crucial role in their progression. Despite its importance, the utilization of videocapillaroscopy in the oral cavity encounters limitations due to the acquisition setup, encompassing spatial and temporal resolutions of the video camera, objective magnification, and physical probe dimensions. Moreover, the operator’s influence during the acquisition process, particularly how the probe is maneuvered, further affects its effectiveness. This study aims to address these challenges and improve data reliability by developing a computerized support system for microcirculation analysis. The designed system performs stabilization, enhancement and automatic segmentation of capillaries in oral mucosal video sequences. The stabilization phase was performed by means of a method based on the coupling of seed points in a classification process. The enhancement process implemented was based on the temporal analysis of the capillaroscopic frames. Finally, an automatic segmentation phase of the capillaries was implemented with the additional objective of quantitatively assessing the signal improvement achieved through the developed techniques. Specifically, transfer learning of the renowned U-net deep network was implemented for this purpose. The proposed method underwent testing on a database with ground truth obtained from expert manual segmentation. The obtained results demonstrate an achieved Jaccard index of 90.1% and an accuracy of 96.2%, highlighting the effectiveness of the developed techniques in oral capillaroscopy. In conclusion, these promising outcomes encourage the utilization of this method to assist in the diagnosis and monitoring of conditions that impact microcirculation, such as rheumatologic or cardiovascular disorders