Featured circular profile for vessel thresholding

To analyze the characteristics of the blood vessels quantitatively is of great importance to the diagnosis of different diseases, e.g., stenosis, diabetic retinopathy, tumor and so on. However, the occurrence of imaging noise or illumination variation may include difficulty for image analysis, especially for accurate quantitative analysis. Therefore, more efficient algorithms for vessel image thresholding, segmentation or extraction have been studied. Nevertheless, these methods may still fail entirely in case of images with large noise or structural noise (such as spots that obscure and/or are brighter than vessels). To improve the algorithm performance on these issues, we propose a novel thresholding algorithm for vascular images by probing the polarity in the circular profiles (PCP) of image pixels. This can robustly distinguish tube-like objects from both cloud-like contaminations and structural noise. Extensive simulation studies based on multiple evaluation criteria suggest that the PCP algorithm typically has a superior performance over other representative approaches. Finally, we also demonstrate the satisfactory performance of the PCP method on real image data.