Image segmentation is a challenging task in the field of image processing and computer vision. In order to obtain an accurate segmentation performance, user interaction is always used in practical image-segmentation applications. However, a good segmentation method should not rely on much prior information. In this paper, an efficient superpixel-guided interactive image-segmentation algorithm based on graph theory is proposed. In this algorithm, we first perform the initial segmentation by using the MeanShift algorithm, then a graph is built by taking the pre-segmented regions (superpixels) as nodes, and the maximum flow–minimum cut algorithm is applied to get the superpixel-level segmentation solution. In this process, each superpixel is represented by a color histogram, and the Bhattacharyya coefficient is chosen to calculate the similarity between any two adjacent superpixels. Considering the over-segmentation problem of the MeanShift algorithm, a narrow band is constructed along the contour of objects using a morphology operator. In order to further segment the pixels around edges accurately, a graph is created again for those pixels in the narrow band and, following the maximum flow–minimum cut algorithm, the final pixel-level segmentation is completed. Extensive experimental results show that the presented algorithm obtains much more accurate segmentation results with less user interaction and less running time than the widely used GraphCut algorithm, Lazy Snapping algorithm, GrabCut algorithm and a region merging algorithm based on maximum similarity (MSRM).

Semi-supervised medical image segmentation algorithms hold significant research and practical value due to their ability to reduce labeling dependency and annotation costs. However, most current algorithms lack diverse regularization methods to effectively exploit robust knowledge from unlabeled data. The pseudo-label filtering methods employed are often overly simplistic, which exacerbates the serious category imbalance problem in medical images. Additionally, these algorithms fail to provide robust semantic representations for comparative learning in multi-scenario settings, making it challenging for the model to learn more discriminative semantic information. To address these issues, we propose a semi-supervised medical image segmentation algorithm that utilizes dual-branch mixup-decoupling confidence training to establish a dual-stream semantic link between labeled and unlabeled images, thereby alleviating semantic ambiguity. Furthermore, we design a bidirectional confidence contrast learning method to maximize the consistency between similar pixels and the distinction between dissimilar pixels in both directions across different feature embeddings in dual views. This enables the model to learn the key features of intra-class similarity and inter-class separability. We conduct a series of experiments on both 2D and 3D datasets, and the experimental results demonstrate that the proposed algorithm achieves notable segmentation performance, outperforming other recent state-of-the-art algorithms.