Secure medical digital twin via human-centric interaction and cyber vulnerability resilience

As a fundamental service in near future, medical digital twin (MDT) is the virtual replica of a person. MDT applies new technologies of IoT, AI and big data to predict the state of health and offer clinical suggestions. It is crucial to secure medical digital twins through deep understanding of the design of digital twins and applying the new vulnerability tolerant approach. In this paper, we present a new medical digital twin, which systematically combines Haptic-AR navigation and deep learning techniques to achieve virtual replica and cyber–human interaction. We report an innovative study of the cyber–human interaction performance in different scenarios. With the focus on cyber resilience, a new solution of vulnerability tolerant is the must in the real-world MDT scenarios. We propose a novel scheme for recognising and fixing MDT vulnerabilities, in which a new CodeBERT-based neural network is applied to better understand risky code and capture cybersecurity semantics. We develop a prototype of the new MDT and collect several real-world datasets. In the empirical study, a number of well-designed experiments are conducted to evaluate the performance of digital twin, cyber–human interaction and vulnerability detection. The results confirm that our new platform works well, can support clinical decision and has great potential in cyber resilience

Magnetic cell separation

Magnetic cell separation, also termed magnetic-activated cell sorting, refers to the separation technology that employs magnetic fields to differentiate various cell populations and isolates target cells from the biological sample. Due to the superior advantages such as low cost, simple operation, high selectivity, high throughput, robustness, and good biocompatibility, magnetic cell separation technology has been developed rapidly, and a variety of magnetic cell separation designs have been proposed. As a very promising candidate for cell separation, magnetic cell separation has been successfully applied for a wide range of biomedical applications. This chapter discusses the fundamental physics behind magnetic cell separation, elaborates the typical formats of developed technologies, and summarizes their key applications of cell separation. This chapter is expected to help readers to have a clear concept of magnetic cell separation, to understand its fundamental physics, and to familiarize typical designs of magnetic cell separators and their applications

Multi-view texture classification using hierarchical synthetic images

Multi-view texture classification is a very challenging task since the view-point variation often leads to the inconsistent local texton patterns. Existing studies focus on the extraction of scale, rotation or affine invariant representations by some specially designed invariant measurements or local descriptors. Differently, in this paper, we propose another framework for multi-view texture classification. A number of synthetic images are hierarchically created to enlarge the training dataset to cover the possible variations of different view-points. Then, a classifier based on random forests is trained based on these synthetic images. In the classification stage, we also create synthetic images for each testing image, and the synthetic images are classified with the pre-trained classifier. The final decision for this testing image is made by the majority voting of the classification results of all these synthetic images. The classification performance is evaluated on the UIUC texture dataset. Our method achieves the classification rate of 99.21%, which is higher than most of the state-of-the-arts

Continuous rotation invariant local descriptors for texton dictionary-based texture classification

Texton dictionary-based texture representation approaches have been proven to be effective for texture classification. We propose two types of local descriptors based on Gaussian derivatives filters, both of them have the property of continuous rotation invariance. The first descriptor directly uses the maximum of the filter responses named continuous maximum responses (CMR). The second descriptor rectifies the filter responses to calculate principal curvatures (PC) of the image surface. The texton dictionary is learned from the training images by clustering the local descriptors, and the representation of each image is the frequency histogram of the textons. The classification results compared with some other popular methods on the CUReT, KTH-TIPS and KTH-TIPS2-a datasets show that representation based on CMR achieves best classification result on the CUReT dataset. The representation based on PC achieves the best classification results on the KTH-TIPS and KTH-TIPS2-a datasets, and the classification performance is robust on different datasets. The experiments of rotation invariant analysis implemented on the Brodatz album illustrate that the CMR descriptor has good inter-class distinguish ability and PC descriptor has strong intra-class congregate ability. The results demonstrate that the proposed local descriptors achieve remarkable performance to classify the rotated textures. © 2012 Elsevier Inc. All rights reserved

A noisy-smoothing relevance feedback method for content-based medical image retrieval

In this paper, we address a new problem of noisy images which present in the procedure of relevance feedback for medical image retrieval. We concentrate on the noisy images, caused by the users mislabeling some irrelevant images as relevant ones, and a noisy-smoothing relevance feedback (NS-RF) method is proposed. In NS-RF, a two-step strategy is proposed to handle the noisy images. In step 1, a noisy elimination algorithm is adopted to identify and eliminate the noisy images. In step 2, to further alleviate the influence of noisy images, a fuzzy membership function is employed to estimate the relevance probabilities of retained relevant images. After noisy handling, the fuzzy support vector machine, which can take into account different relevant images with different relevance probabilities, is adopted to re-rank the images. The experimental results on the IRMA medical image collection demonstrate that the proposed method can deal with the noisy images effectively. © 2013 Springer Science+Business Media New York

Inertial Microfluidics: Mechanisms and Applications

Inertial Microfluidics: Mechanisms and Application

Guest Editorial: Large-scale 3D Multimedia Analysis and Applications

Guest Editorial: Large-scale 3D Multimedia Analysis and Application

Scale invariant texture representation based on frequency decomposition and gradient orientation

This paper proposes an effective scale invariant texture representation based on frequency decomposition and gradient orientation. First, the image intensities are decomposed into different orientations by using wedge filters in the frequency domain, and the N-nary coding method is adopted for the vector quantization. Second, the scale invariant gradient orientation is generated by selecting the most stable value of the gradient orientation with different Gaussian scales. Finally, the 2D joint distribution of the two types of local descriptors is used as the representation. The performance was evaluated on texture classification using a nearest neighbor classifier. Simple but not ordinary, our method achieves state of the art classification performance on the KTH-TIPS dataset under the traditional experimental design. Moreover, the main experiments were conducted on the KTH-TIPS and KTH-TIPS2-b datasets with the experimental designs of scale invariance validation. Compared with the methods of basic image features (BIFs) and local energy pattern (LEP), the proposed representation achieves superior performance with much lower dimension of representation

Local energy pattern for texture classification using self-adaptive quantization thresholds

Local energy pattern, a statistical histogram-based representation, is proposed for texture classification. First, we use normalized local-oriented energies to generate local feature vectors, which describe the local structures distinctively and are less sensitive to imaging conditions. Then, each local feature vector is quantized by self-adaptive quantization thresholds determined in the learning stage using histogram specification, and the quantized local feature vector is transformed to a number by N-nary coding, which helps to preserve more structure information during vector quantization. Finally, the frequency histogram is used as the representation feature. The performance is benchmarked by material categorization on KTH-TIPS and KTH-TIPS2-a databases. Our method is compared with typical statistical approaches, such as basic image features, local binary pattern (LBP), local ternary pattern, completed LBP, Weber local descriptor, and VZ algorithms (VZ-MR8 and VZ-Joint). The results show that our method is superior to other methods on the KTH-TIPS2-a database, and achieving competitive performance on the KTH-TIPS database. Furthermore, we extend the representation from static image to dynamic texture, and achieve favorable recognition results on the University of California at Los Angeles (UCLA) dynamic texture database. © 1992-2012 IEEE

Influence of void space on microscopic behavior of fluid flow in rock joints

Advanced microfluidic technology was used to examine the microscopic viscous and inertial effects evolution of water flow in rock joints. The influence of void space on fluid flow behaviour in rock joints under different flow velocities was experimentally investigated at the micro scale. Using advanced fabrication technology of microfluidic device, micro flow channels of semicircular, triangular, rectangular and pentagonal cavities were fabricated to simulate different void space of rock joints, respectively. Using the fluorescence labelling approach, the trajectory of water flow was captured by the microscope digital camera when it passed over the cavity under different flow velocities. The flow tests show that the flow trajectory deviated towards the inside of the cavity at low flow velocities. With the increase in flow velocity, this degree of flow trajectory deviation decreased until there was no trajectory deviation for flow in the straight parallel channel. The flow trajectory deviation initially reduced from the void corner near the entrance. At the same time, a small eddy appeared near the void corner of the entrance. The size and intensity of the eddy increased with the flow velocity until it occupied the whole cavity domain. The gradual reduction of flow trajectory near the straight parallel channel and the growth of eddy inside the cavity reflect the evolution of microscopic viscous and inertial forces under different flow velocities. The eddy formed inside the cavity does not contribute to the total flow flux, but the running of the eddy consumes flow energy. This amount of pressure loss due to voids could contribute to the nonlinear deviation of fracture fluid flow from Darcy's law. This study contributes to the fundamental understanding of non-Darcy's flow occurrence in rock joints at the micro scale. © 2014 Published by Elsevier B.V. on behalf of China University of Mining & Technology