Near-infrared vascular image segmentation using improved level set method

Abstract

Near-infrared vascular images are crucial for vascular disease diagnosis and treatment. To solve the problems of difficult segmentation and inaccurate computation issues, we propose a series of image processing methods. First, the images are preprocessed, including background removal, contrast stretching, and noise suppression. After that, the images are enhanced with a two-stage image enhancement method, which successfully combines the benefits of convolutional neural network and traditional image enhancement method. Finally, the images are segmented by an Adaptive Prior Shape Level Set Evolution (APSLSE) method, which effectively improves the common problems of initial contour sensitivity and unidirectional variation of area terms. A shape restriction item is designed to the level set function to improve its suitability for the segmentation of blood vessels. A dataset of 360 images is collected for the research and validation of above algorithms. Extensive experimental results show that the proposed algorithms can effectively process the poor quality near-infrared blood vessel images and segment their vascular shape. Compared the segmentation results with those manually labelled by experts, the False Negative Rate (FNR) is 0.1930 and False Positive Rate (FPR) is 0.04633.