The fifth-generation (5G) networks can provide high data rates, ultralow latency and huge network capacity. In 5G networks environment, the popularity of the Internet of Things (IoT) has led to a rapid increase in the amount of data. Multiserver distributed cloud computing technology provides an excellent solution to alleviate network pressure caused by the rapid growth of data. However, this technology serves as a two-edged weapon, which not only makes various IoT applications possible but also brings growing concerns for user privacy and ever pressing security challenges. To ensure the high security of 5G network-based applications, we design a secure user anonymity-preserving biometrics and PUFs-based multiserver authentication scheme with key agreement. In our method, we make full use of the inherent security features of user fingerprint and smart device PUF to design a secure multiserver authentication scheme with key agreement in 5G Networks. The proposed scheme is able to resist recognized attacks and its robustness has been verified by security analysis.

Accurate segmentation of lesions plays a critical role in medical image analysis and diagnosis. Traditional segmentation approaches that rely solely on visual features often struggle with the inherent uncertainty in lesion distribution and size. To address these issues, we propose STPNet, a Scale-aware Text Prompt Network that leverages vision-language modeling to enhance medical image segmentation. Our approach utilizes multi-scale textual descriptions to guide lesion localization and employs retrieval-segmentation joint learning to bridge the semantic gap between visual and linguistic modalities. Crucially, STPNet retrieves relevant textual information from a specialized medical text repository during training, eliminating the need for text input during inference while retaining the benefits of cross-modal learning. We evaluate STPNet on three datasets: COVID-Xray, COVID-CT, and Kvasir-SEG. Experimental results show that our vision-language approach outperforms state-of-the-art segmentation methods, demonstrating the effectiveness of incorporating textual semantic knowledge into medical image analysis. The code has been made publicly on https://github.com/HUANGLIZI/STPNet.

Three-dimensional reconstruction from multiview images is considered as a longstanding problem in computer vision and graphics. In order to achieve high-fidelity geometry and appearance of 3-D scenes, this article proposes a novel geometric object learning method for multiview reconstruction withfuzzy set theory. We establish anew neural 3D reconstruction theoretical framecalled neural fuzzy geometric representation (NeuFG), which is a special type of implicit representation of geometric scene that only takes value in [0, 1]. NeuFG is essentially a volume image, and thus can be visualized directly with the conventional volume rendering technique. Extensive experiments on two public datasets, i.e., DTU and BlendedMVS, show that our method has the ability of accurately reconstructing complex shapes with vivid geometric details, without the requirement of mask supervision. Both qualitative and quantitative comparisons demonstrate that the proposed method has superior performance over the state-of-the-art neural scene representation methods. The code will be released on GitHub soon.

Deep learning has been widely used in medical image segmentation and other aspects. However, the performance of existing medical image segmentation models has been limited by the challenge of obtaining sufficient high-quality labeled data due to the prohibitive data annotation cost. To alleviate this limitation, we propose a new text-augmented medical image segmentation model LViT (Language meets Vision Transformer). In our LViT model, medical text annotation is incorporated to compensate for the quality deficiency in image data. In addition, the text information can guide to generate pseudo labels of improved quality in the semi-supervised learning. We also propose an Exponential Pseudo label Iteration mechanism (EPI) to help the Pixel-Level Attention Module (PLAM) preserve local image features in semi-supervised LViT setting. In our model, LV (Language-Vision) loss is designed to supervise the training of unlabeled images using text information directly. For evaluation, we construct three multimodal medical segmentation datasets (image + text) containing X-rays and CT images. Experimental results show that our proposed LViT has superior segmentation performance in both fully-supervised and semi-supervised setting. The code and datasets are available at https://github.com/HUANGLIZI/LViT.

Most recent scribble-supervised segmentation methods commonly adopt a CNN framework with an encoder-decoder architecture. Despite its multiple benefits, this framework generally can only capture small-range feature dependency for the convolutional layer with the local receptive field, which makes it difficult to learn global shape information from the limited information provided by scribble annotations. To address this issue, this paper proposes a new CNN-Transformer hybrid solution for scribble-supervised medical image segmentation called ScribFormer. The proposed ScribFormer model has a triple-branch structure, i.e., the hybrid of a CNN branch, a Transformer branch, and an attention-guided class activation map (ACAM) branch. Specifically, the CNN branch collaborates with the Transformer branch to fuse the local features learned from CNN with the global representations obtained from Transformer, which can effectively overcome limitations of existing scribble-supervised segmentation methods. Furthermore, the ACAM branch assists in unifying the shallow convolution features and the deep convolution features to improve model's performance further. Extensive experiments on two public datasets and one private dataset show that our ScribFormer has superior performance over the state-of-the-art scribble-supervised segmentation methods, and achieves even better results than the fully-supervised segmentation methods. The code is released at https://github.com/HUANGLIZI/ScribFormer.

Detecting pneumonia, especially coronavirus disease 2019 (COVID-19), from chest X-ray (CXR) images is one of the most effective ways for disease diagnosis and patient triage. The application of deep neural networks (DNNs) for CXR image classification is limited due to the small sample size of the well-curated data. To tackle this problem, this article proposes a distance transformation-based deep forest framework with hybrid-feature fusion (DTDF-HFF) for accurate CXR image classification. In our proposed method, hybrid features of CXR images are extracted in two ways: hand-crafted feature extraction and multigrained scanning. Different types of features are fed into different classifiers in the same layer of the deep forest (DF), and the prediction vector obtained at each layer is transformed to form distance vector based on a self-adaptive scheme. The distance vectors obtained by different classifiers are fused and concatenated with the original features, then input into the corresponding classifier at the next layer. The cascade grows until DTDF-HFF can no longer gain benefits from the new layer. We compare the proposed method with other methods on the public CXR datasets, and the experimental results show that the proposed method can achieve state-of-the art (SOTA) performance. The code will be made publicly available at https://github.com/hongqq/DTDF-HFF.

Segmentation of COVID-19 lesions can assist physicians in better diagnosis and treatment of COVID-19. However, there are few relevant studies due to the lack of detailed information and high-quality annotation in the COVID-19 dataset. To solve the above problem, we propose C2FVL, a Coarse-to-Fine segmentation framework via Vision-Language alignment to merge text information containing the number of lesions and specific locations of image information. Introducing text information allows the network to achieve better prediction results on challenging datasets. We conduct extensive experiments on two COVID-19 datasets, including chest X-ray and CT, and the results demonstrate that our proposed method outperforms other state-of-the-art segmentation methods.

Deep learning has been used in many computer-vision-based industrial Internet of Things applications. However, deep neural networks are vulnerable to adversarial examples that have been crafted specifically to fool a system while being imperceptible to humans. In this article, we propose a consensus defense (Cons-Def) method to defend against adversarial attacks. Cons-Def implements classification and detection based on the consensus of the classifications of the augmented examples, which are generated based on an individually implemented intensity exchange on the red, green, and blue components of the input image. We train a CNN using augmented examples together with their original examples. For the test image to be assigned to a specific class, the class occurrence of the classifications on its augmented images should be the maximum and reach a defined threshold. Otherwise, it is detected as an adversarial example. The comparison experiments are implemented on MNIST, CIFAR-10, and ImageNet. The average defense success rate (DSR) against white-box attacks on the test sets of the three datasets is 80.3%. The average DSR against black-box attacks on CIFAR-10 is 91.4%. The average classification accuracies of Cons-Def on benign examples of the three datasets are 98.0%, 78.3%, and 66.1%. The experimental results show that Cons-Def shows a high classification performance on benign examples and is robust against white-box and black-box adversarial attacks.

Cardiac medical image segmentation plays an important role in the diagnosis and clinical treatment of cardiovascular diseases. However, due to the variability of cardiac anatomy and the ambiguity between cardiac substructures, it is still difficult to quickly segment the entire heart from medical images. Most of the current researches utilize neural network structure to perform whole heart segmentation. Although good segmentation accuracy has been achieved, it usually requires a long training time. This paper aims to build a new whole heart segmentation model based on Deep Forest, called Multi-Resolution Deep Forest Framework(MRDFF), which performs segmentation through two stages. In the first stage, the heart region is located by rough binary classification, and similarity screening is used to reduce redundancy. The second stage subdivides the heart substructures based on the results of the first stage and uses multi-scale fusion to achieve high segmentation accuracy. The experimental results conducted on the public data set MM-WHS show that under the same training data and configuration, our model can be trained in only 4.5 hours, which is about 1/2 of the training time of neural network models, and can reach the accuracy not lower than neural network models, which shows the feasibility and efficiency of our model. The code will be made publicly available at https://github.com/xufeixf/MRDFF.

Vasculature geometry reconstruction from volumetric medical data is a crucial task in the development of computer guided minimally invasive vascular surgery systems. In this paper, a technique for reconstructing the geometry of vasculatures using bivariate implicit splines is developed. With the proposed technique, an implicit geometry representation of the vascular tree can be accurately constructed based on the voxels extracted directly from the surface of a certain vascular structure in a given volumetric medical dataset. Experimental results show that the geometric representation built using our method can faithfully represent the morphology and topology of vascular structures. In addition, both the qualitative and the quantitative validations have been performed to show that the reconstructed vessel geometry is of high accuracy and smoothness. An virtual angioscopy system has been implemented to indicate one of the strengths of our proposed method.

In volume rendering, most optical models currently in use are based on the assumptions that a volumetric object is a collection of particles and that the macro behavior of particles, when they interact with light rays, can be predicted based on the behavior of each individual particle. However, such models are not capable of characterizing the collective optical effect of a collection of particles which dominates the appearance of the boundaries of dense objects. In this paper, we propose a generalized optical model that combines particle elements and surface elements together to characterize both the behavior of individual particles and the collective effect of particles. The framework based on a new model provides a more powerful and flexible tool for hybrid rendering of isosurfaces and transparent clouds of particles in a single scene. It also provides a more rational basis for shading, so the problem of normal-based shading in homogeneous regions encountered in conventional volume rendering can be easily avoided. The model can be seen as an extension to the classical model. It can be implemented easily, and most of the advanced numerical estimation methods previously developed specifically for the particle-based optical model, such as preintegration, can be applied to the new model to achieve high-quality rendering results.

Volume rendering has long been used as a key technique for volume data visualization, which works by using a transfer function to map color and opacity to each voxel. Many volume rendering approaches proposed so far for voxels classification have been limited in a single global transfer function, which is in general unable to properly visualize interested structures. In this paper, we propose a localized volume data visualization approach which regards volume visualization as a combination of two mutually related processes: the segmentation of interested structures and the visualization using a locally designed transfer function for each individual structure of interest. As shown in our work, a new interactive segmentation algorithm is advanced via skeletons to properly categorize interested structures. In addition, a localized transfer function is subsequently presented to assign optical parameters via interested information such as intensity, thickness and distance. As can be seen from the experimental results, the proposed techniques allow to appropriately visualize interested structures in highly complex volume medical data sets.

2D splines are a powerful tool for shape modeling, either parametrically or implicitly. However, compared with regular grid-based tensor-product splines, most of the high-dimensional spline techniques based on nonregular 2D polygons, such as box spline and simplex spline, are generally very expensive to evaluate. Though they have many desirable mathematical properties and have been proved theoretically to be powerful in graphics modeling, they are not a convenient graphics modeling technique in terms of practical implementation. In shape modeling practice, we still lack a simple and practical procedure in creating a set of bivariate spline basis functions from an arbitrarily specified 2D polygonal mesh. Solving this problem is of particular importance in using 2D algebraic splines for implicit modeling, as in this situation underlying implicit equations need to be solved quickly and accurately. In this article, a new type of bivariate spline function is introduced. This newly proposed type of bivariate spline function can be created from any given set of 2D polygons that partitions the 2D plane with any required degree of smoothness. In addition, the spline basis functions created with the proposed procedure are piecewise polynomials and can be described explicitly in analytical form. As a result, they can be evaluated efficiently and accurately. Furthermore, they have all the good properties of conventional 2D tensor-product-based B-spline basis functions, such as non-negativity, partition of unit, and convex-hull property. Apart from their obvious use in designing freeform parametric geometric shapes, the proposed 2D splines have been shown a powerful tool for implicit shape modeling.

In this paper, a sufficient condition for a quadric surface to be an ellipsoid has been developed and a closed-form solution for ellipsoid fitting is developed based on this constraint, which is a best fit to the given data amongst those ellipsoids whose short radii are at least half of their major radii, in the sense of algebraic distance. A simple search procedure is proposed to pursuit the 'best' ellipsoid when data cannot be well described by this type of ellipsoid. The proposed fitting algorithm is quick, stable and insensitive to small errors in the data.

Abstract Implicit planar curve and surface fitting to a set of scattered points plays an important role in solving a wide variety of problems occurring in computer graphics modelling, computer graphics animation, and computer assisted surgery. The fitted implicit surfaces can be either algebraic or non‐algebraic. The main problem with most algebraic surface fitting algorithms is that the surface fitted to a given data set is often unbounded, multiple sheeted, and disconnected when a high degree polynomial is used, whereas a low degree polynomial is too simple to represent general shapes. Recently, there has been increasing interest in non‐algebraic implicit surface fitting. In these techniques, one popular way of representing an implicit surface has been the use of radial basis functions. This type of implicit surface can represent various shapes to a high level of accuracy. In this paper, we present an implicit surface fitting algorithm using radial basis functions with an ellipsoid constraint. This method does not need to build interior and exterior layers for the given data set or to use information on surface normal but still can fit the data accurately. Furthermore, the fitted shape can still capture the main features of the object when the data sets are extremely sparse. The algorithm involves solving a simple general eigen‐system and a computation of the inverse or psedo‐inverse of a matrix, which is straightforward to implement.