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Abstract Magnetic resonance imaging is a medical imaging technique to create comprehensive images of the tissues and organs in the body. This study presents an advanced approach for storing and compressing neuroimaging informatics technology initiative files, a standard format in magnetic resonance imaging. It is designed to enhance telemedicine services by facilitating efficient and high-quality communication between healthcare practitioners and patients. The proposed downsampling approach begins by opening the neuroimaging informatics technology initiative file as volumetric data and then planning it into several slice images. Then, the quantization hiding technique will be applied to each of the two consecutive slice images to generate the stego slice with the same size. This involves the following major steps: normalization, microblock generation, and discrete cosine transformation. Finally, it assembles the resultant stego slice images to produce the final neuroimaging informatics technology initiative file as volumetric data. The upsampling process, designed to be completely blind, reverses the downsampling steps to reconstruct the subsequent image slice accurately. The efficacy of the proposed method was evaluated using a magnetic resonance imaging dataset, focusing on peak signal-to-noise ratio, signal-to-noise ratio, structural similarity index, and Entropy as key performance metrics. The results demonstrate that the proposed approach not only significantly reduces file sizes but also maintains high image quality.

Abstract Purpose Convolution operator-based neural networks have shown great success in medical image segmentation over the past decade. The U-shaped network with a codec structure is one of the most widely used models. Transformer, a technology used in natural language processing, can capture long-distance dependencies and has been applied in Vision Transformer to achieve state-of-the-art performance on image classification tasks. Recently, researchers have extended transformer to medical image segmentation tasks, resulting in good models. Methods This review comprises publications selected through a Web of Science search. We focused on papers published since 2018 that applied the transformer architecture to medical image segmentation. We conducted a systematic analysis of these studies and summarized the results. Results To better comprehend the benefits of convolutional neural networks and transformers, the construction of the codec and transformer modules is first explained. Second, the medical image segmentation model based on transformer is summarized. The typically used assessment markers for medical image segmentation tasks are then listed. Finally, a large number of medical segmentation datasets are described. Conclusion Even if there is a pure transformer model without any convolution operator, the sample size of medical picture segmentation still restricts the growth of the transformer, even though it can be relieved by a pretraining model. More often than not, researchers are still designing models using transformer and convolution operators.

Abstract Deep learning recently achieved advancement in the segmentation of medical images. In this regard, U-Net is the most predominant deep neural network, and its architecture is the most prevalent in the medical imaging society. Experiments conducted on difficult datasets directed us to the conclusion that the traditional U-Net framework appears to be deficient in certain respects, despite its overall excellence in segmenting multimodal medical images. Therefore, we propose several modifications to the existing cutting-edge U-Net model. The technical approach involves applying a Multi-Dimensional U-Convolutional Neural Network to achieve accurate segmentation of multimodal biomedical images, enhancing precision and comprehensiveness in identifying and analyzing structures across diverse imaging modalities. As a result of the enhancements, we propose a novel framework called Multi-Dimensional U-Convolutional Neural Network (MDU-CNN) as a potential successor to the U-Net framework. On a large set of multimodal medical images, we compared our proposed framework, MDU-CNN, to the classical U-Net. There have been small changes in the case of perfect images, and a huge improvement is obtained in the case of difficult images. We tested our model on five distinct datasets, each of which presented unique challenges, and found that it has obtained a better performance of 1.32%, 5.19%, 4.50%, 10.23% and 0.87%, respectively.

Abstract In the intricate network environment, the secure transmission of medical images faces challenges such as information leakage and malicious tampering, significantly impacting the accuracy of disease diagnoses by medical professionals. To address this problem, the authors propose a robust feature watermarking algorithm for encrypted medical images based on multi‐stage discrete wavelet transform (DWT), Daisy descriptor, and discrete cosine transform (DCT). The algorithm initially encrypts the original medical image through DWT‐DCT and Logistic mapping. Subsequently, a 3‐stage DWT transformation is applied to the encrypted medical image, with the centre point of the LL3 sub‐band within its low‐frequency component serving as the sampling point. The Daisy descriptor matrix for this point is then computed. Finally, a DCT transformation is performed on the Daisy descriptor matrix, and the low‐frequency portion is processed using the perceptual hashing algorithm to generate a 32‐bit binary feature vector for the medical image. This scheme utilises cryptographic knowledge and zero‐watermarking technique to embed watermarks without modifying medical images and can extract the watermark from test images without the original image, which meets the basic requirements of medical image watermarking. The embedding and extraction of watermarks are accomplished in a mere 0.160 and 0.411s, respectively, with minimal computational overhead. Simulation results demonstrate the robustness of the algorithm against both conventional attacks and geometric attacks, with a notable performance in resisting rotation attacks.

Memristors are considered one of the most promising new-generation memory technologies due to their high integration density, fast read/write speeds, and ultra-low power consumption. Natural biomaterials have attracted interest in integrated circuits and electronics because of their environmental friendliness, sustainability, low cost, and excellent biocompatibility. In this study, a sustainable biomemristor with Ag/mugwort:PVDF/ITO structure was prepared using spin-coating and magnetron sputtering methods, which exhibited excellent durability, significant resistance switching (RS) behavior and unidirectional conduction properties when three metals were used as top electrode. By studying the conductivity mechanism of the device, a charge conduction model was established by the combination of F-N tunneling, redox, and complexation reaction. Finally, the novel logic gate circuits were constructed using the as-prepared memristor, and further memristor based encryption circuit using 3-8 decoder was innovatively designed, which can realize uniform rule encryption and decryption of medical information for data and medical images. Therefore, this work realizes the integration of memristor with traditional electronic technology and expands the applications of sustainable biomemristors in digital circuits, data encryption, and medical image security.

Medical image compression is vital for preserving image quality and optimizing storage and transmission in healthcare facilities. FPGA design faces challenges in balancing compression ratios, speed, and power consumption. This study proposes a novel approach using the Selfish Herd Optimization (SHO) algorithm to cluster medical image data based on anatomical features. A hybrid deep convolutional neural network generates optimal predictors for each cluster, extracting features from the image data and generating predictions for each pixel based on its neighbors. The proposed system achieves low power and high-speed FPGA design while ensuring reliability and accuracy. Experimental results on Virtex-7 VC709 and Virtex-5QV130FX FPGAs demonstrate impressive improvements, including a 244.08% increase in frequency, 210.04% increase in speed, 200.90% increase in throughput, 61.37% increase in efficiency, and significant reductions in resource utilization and power consumption. This design promises efficient lossless medical image compression with enhanced performance. [ABSTRACT FROM AUTHOR]

In order to aim for diagnosis of disease and decision making, the medical imaging plays an important role in health science. Clinical pictures are portrayals of profoundly vulnerable computerized images, those can be tampered without leaving any visual clues. Hence it is challenging to keep up its credibility. However, as there are numerous ways to manipulate an image, correspondingly various strategies have also been proposed to safeguard the genuineness of medical images. This survey paper presents different techniques used for medical image authentication viz. watermarking, signature and hybrid techniques. The state-of-the-art techniques have attained promising results for authentication and tampering detection, but an efficient tampering localization and recovery have remained still as a challenge. This review article can be considered as a benchmark survey paper as it gives a complete comprehensive overview commencing from the evolution of medical image formats, types of medical imaging modalities and also provides an elaborative comparison over 40 research works in terms of prominent factors like type of medical image used, embedded region, embedded data, about the coverage of tampering localization and recovery along with the discussion on limitations and future works of each one. [ABSTRACT FROM AUTHOR]

In recent years, deep learning algorithms have achieved remarkable results in medical image segmentation. These networks with an enormous number of parameters often encounter challenges in handling image boundaries and details, which may result in suboptimal segmentation results. To solve the problem, we develop atrous spatial pyramid pooling (ASPP) and combine it with the squeeze‐and‐excitation block (SE block), as well as present the PS module, which employs a broader and multiscale receptive field at the network's bottom to obtain more detailed semantic information. We also propose the local guided block (LG block) and also its combination with the SE block to form the LS block, which can obtain more abundant local features in the feature map, so that more edge information can be retained in each down sampling process, thereby improving the performance of boundary segmentation. We propose PLU‐Net and integrate our PS module and LS block into U‐Net. We put our PLU‐Net to the test on three benchmark datasets, and the results show that by fewer parameters and FLOPs, it outperforms on medical semantic segmentation tasks. [ABSTRACT FROM AUTHOR]

Deep learning is revolutionizing radiology report generation (RRG) with the adoption of vision encoder–decoder (VED) frameworks, which transform radiographs into detailed medical reports. Traditional methods, however, often generate reports of limited diversity and struggle with generalization. Our research introduces reinforcement learning and text augmentation to tackle these issues, significantly improving report quality and variability. By employing RadGraph as a reward metric and innovating in text augmentation, we surpass existing benchmarks like BLEU4, ROUGE-L, F1CheXbert, and RadGraph, setting new standards for report accuracy and diversity on MIMIC-CXR and Open-i datasets. Our VED model achieves F1-scores of 66.2 for CheXbert and 37.8 for RadGraph on the MIMIC-CXR dataset, and 54.7 and 45.6 , respectively, on Open-i. These outcomes represent a significant breakthrough in the RRG field. The findings and implementation of the proposed approach, aimed at enhancing diagnostic precision and radiological interpretations in clinical settings, are publicly available on GitHub to encourage further advancements in the field. [ABSTRACT FROM AUTHOR]

Recent advancements in computational performance and medical simulation technology have made significant strides, particularly in predictive diagnosis. This study focuses on the blood flow simulation reduced-order models, which provide swift and cost-effective solutions for complex vascular systems, positioning them as practical alternatives to 3D simulations in resource-limited medical settings. The paper introduces a visualization platform for patient-specific and image-based 1D–0D simulations. This platform covers the entire workflow, from modeling to dynamic 3D visualization of simulation results. Two case studies on, respectively, carotid stenosis and arterial remodeling demonstrate its utility in blood flow simulation applications. [ABSTRACT FROM AUTHOR]

The diverse shapes and scales, complicated backgrounds, blurred boundaries, and similar appearances challenge the current organ segmentation methods in medical scene images. It is difficult to acquire satisfactory performance to directly extend the object segmentation methods in the natural scene images to the medical scene images. In this paper, we propose a discriminant feature pyramid (DFPNet) network for organ segmentation in the original medical images, which consists of two sub-networks: the feature steered network and the border network. To be specific, the feature steered network takes a top-down step-wise manner to extract abundant context information, which is conducive to suppressing the cluttered background and perceiving the scale variation of objects. The border network utilizes a bottom-up step-wise manner to optimize the boundary feature map, which aims at distinguishing adjacent edge features with similar appearances but diverse labels. A series of experiments were conducted on three publicly available medical datasets (i.e., LUNA 16, RIM-ONE-R1, and VNC datasets) to evaluate the validity and generalization of the proposed DFPNet. Experimental results indicate that our network achieves superior performance in terms of the receiver operating characteristic (ROC) curve, F-Score, Jaccard index, and Hausdorff distance. The code will be available at: https://github.com/Xie-Xiwang/DFPNet. [ABSTRACT FROM AUTHOR]

The security and confidentiality of medical images are of utmost importance due to frequent issues such as leakage, theft, and tampering during transmission and storage, which seriously impact patient privacy. Traditional encryption techniques applied to entire images have proven to be ineffective in guaranteeing timely encryption and preserving the privacy of organ regions separated from the background. In response, this study proposes a specialized and efficient local image encryption algorithm for the medical field. The proposed encryption algorithm focuses on the regions of interest (ROI) within massive medical images. Initially, the Laplacian of Gaussian operator and the outer boundary tracking algorithm are employed to extract the binary image and achieve ROI edge extraction. Subsequently, the image is divided into ROI and ROB (regions outside ROI). The ROI is transformed into a row vector and rearranged using the Lorenz hyperchaotic system. The rearranged sequence is XOR with the random sequence generated by the Henon chaotic map. Next, the encrypted sequence is arranged according to the location of the ROI region and recombined with the unencrypted ROB to obtain the complete encrypted image. Finally, the least significant bit algorithm controlled by the key is used to embed binary image into the encrypted image to ensure lossless decryption of the medical images. Experimental verification demonstrates that the proposed selective encryption algorithm for massive medical images offers relatively ideal security and higher encryption efficiency. This algorithm addresses the privacy concerns and challenges faced in the medical field and contributes to the secure transmission and storage of massive medical images. [ABSTRACT FROM AUTHOR]

Breast cancer (BC) is one of the major principal sources of high mortality among women worldwide. Consequently, early detection is essential to save lives. BC can be diagnosed with different modes of medical images such as mammography, ultrasound, computerized tomography, biopsy, and magnetic resonance imaging. A histopathology study (biopsy) that results in images is often performed to help diagnose and analyze BC. Transfer learning (TL) is a machine-learning (ML) technique that reuses a learning method that is initially built for a task to be applied to a model for a new task. TL aims to enhance the assessment of desired learners by moving the knowledge contained in another but similar source domain. Consequently, the challenge of the small dataset in the desired domain is reduced to build the desired learners. TL plays a major role in medical image analysis because of this immense property. This paper focuses on the use of TL methods for the investigation of BC image classification and detection, preprocessing, pretrained models, and ML models. Through empirical experiments, the EfficientNets pretrained neural network architectures and ML classification models were built. The support vector machine and eXtreme Gradient Boosting (XGBoost) were learned on the BC dataset. The result showed a comparative but good performance of EfficientNetB4 and XGBoost. An outcome based on accuracy, recall, precision, and F1_Score for XGBoost is 84%, 0.80, 0.83, and 0.81, respectively. [ABSTRACT FROM AUTHOR]

This paper presents an algorithm for image super-resolution based on CNN. The objective is to work on medical images including images with tumors for better diagnosis. The CNN is utilized to upscale the images. Three steps are incorporated in the proposed algorithm including the patch extraction and representation which used to extract patches from low-resolution images by pretrained filters, and then nonlinear mapping is used to map nonlinearly each high-dimensional vector. The high-resolution image is obtained in the reconstruction process. The obtained results are evaluated with metrics including PSNR, and the average PSNR for CT is 29.755 and 29.12 for MRI. Results reveal high quality of the obtained images. In addition, a fusion-based step is implemented on super-resolution MRI with CT images using CNNs in fusion, we found that SRCNN gives good results than Bi-cubic interpolation. The comparison for the fusion results with and without super-resolution, SRCNN before fusion improves the results. [ABSTRACT FROM AUTHOR]

Image compression plays a crucial role in the field of medical imaging, including Magnetic Resonance Imaging (MRI). The MRI images are typically large and high-resolution, which results in substantial data storage requirements. Compressing MRI images helps reduce the storage space needed to store the images, making it more efficient and cost-effective to store and transmit them. To overcome these drawbacks, this paper proposes an efficient medical image compression based on hybrid machine learning approaches. There are two main stages are considered in this proposed methodology, named a segmentation stage The Region of Interest (ROI) in the image is recognized by the segmentation stage; and it given to the next stage. Segmentation is carried out by hybrid Grey Wolf Optimization with Fuzzy C-Means (FCM) is proposed to better balance the exploitation and exploration phases of optimization. Then, the neural network i.e., optimized convolutional neural network (Op-CNN), compress the ROI region of the input image depending on the detected segments. Meanwhile, the second region (NROI) is compressed by the Recurrent Neural Networks (RNNs). The suggested method of image compression for medical imaging outcomes and datasets are assessed with highest PSNR value of 45.502, which is higher than the existing techniques. [ABSTRACT FROM AUTHOR]

Medical images are commonly used to determine the location, size, and shape of organs, as well as the scope and physical properties of lesions, which are important bases for intelligent medical diagnosis. Low-quality medical images have serious spots, noise, and weak boundaries between similar tissues, which might affect the clarity of human organs and lesions in the image. This problem seriously hinders doctors' diagnoses and the accuracy of computer-aided detection. Therefore, enhancing the internal texture details of medical images, strengthening tissue boundary information, and suppressing noise are of great significance for experts to diagnose diseases. We propose a medical image super-resolution reconstruction method based on residual attention networks. The method combines channel attention and spatial attention modules to enhance weak boundaries of the tissues and suppress noise. In addition, we introduce the skip connection structure to prevent network feature extraction from causing the loss of shallow feature information. We built three medical image datasets (lung CT images, brain MR images, and transrectal ultrasound (TRUS) images) to evaluate the performance of the proposed method. The results reveal that the proposed method outperforms other methods of medical image reconstruction. Moreover, it accurately reconstructs the internal texture and edge information of medical images while effectively suppressing noise. [ABSTRACT FROM AUTHOR]

This research introduces a novel and robust watermarking scheme for medical Brain MRI DICOM images, addressing the challenge of maintaining high imperceptibility and robustness simultaneously. The scheme ensures privacy control, content authentication, and protection against the detachment of vital Electronic Patient Record information. To enhance imperceptibility, a Dynamic Visibility Threshold parameter leveraging the Human Visual System is introduced. Embeddable Zones and Non-Embeddable Zones are defined to enhance robustness, and an enhanced Knight Tour algorithm based on Slantlet Transform shuffles the embedding sequence for added security. The scheme achieves remarkable results with a Peak Signal-to-Noise Ratio (PSNR) evaluation surpassing contemporary techniques. Extensive experimentation demonstrates resilience to various attacks, with low Bit Error Rate (BER) and high Normalized Cross-Correlation (NCC) values. The proposed technique outperforms existing methods, emphasizing its superior performance and effectiveness in medical image watermarking. [ABSTRACT FROM AUTHOR]

Brain tumors pose a significant medical challenge, demanding early detection and diagnosis to enhance patient outcomes. However, existing brain tumor detection methods often suffer from inefficiency and inaccuracy. Presently, deep learning models have gained prominence in classifying brain MRI images due to their superior accuracy compared to traditional classifiers. Nonetheless, optimizing hyperparameters manually to achieve precise image classification is a daunting and time-consuming task. To address these challenges, we present a novel approach in this paper—the Manta Ray Foraging Optimized Convolutional Neural Network (MRFO-CNN) model to classify brain MRIs into tumorous and non-tumorous categories. We leverage the Manta Ray Foraging Optimization technique to automatically determine the optimal hyperparameters, specifically batch size and epoch settings. Our model is trained on a dataset comprising 3000 brain MR images and validated on an additional 351 brain MR images. The results unequivocally demonstrate the superiority of our proposed MRFO-CNN model over conventional CNN approaches, achieving an impressive training accuracy of 99.3% and a validation accuracy of 98.7%. This research not only showcases the potential of deep learning in brain tumours classification but also underscores the efficiency and effectiveness of automated hyperparameter optimization in medical image analysis. [ABSTRACT FROM AUTHOR]

Magnetic resonance imaging is a medical imaging technique to create comprehensive images of the tissues and organs in the body. This study presents an advanced approach for storing and compressing neuroimaging informatics technology initiative files, a standard format in magnetic resonance imaging. It is designed to enhance telemedicine services by facilitating efficient and high-quality communication between healthcare practitioners and patients. The proposed downsampling approach begins by opening the neuroimaging informatics technology initiative file as volumetric data and then planning it into several slice images. Then, the quantization hiding technique will be applied to each of the two consecutive slice images to generate the stego slice with the same size. This involves the following major steps: normalization, microblock generation, and discrete cosine transformation. Finally, it assembles the resultant stego slice images to produce the final neuroimaging informatics technology initiative file as volumetric data. The upsampling process, designed to be completely blind, reverses the downsampling steps to reconstruct the subsequent image slice accurately. The efficacy of the proposed method was evaluated using a magnetic resonance imaging dataset, focusing on peak signal-to-noise ratio, signal-to-noise ratio, structural similarity index, and Entropy as key performance metrics. The results demonstrate that the proposed approach not only significantly reduces file sizes but also maintains high image quality. [ABSTRACT FROM AUTHOR]

Intracranial aneurysms (IAs) manifest as atypical dilatation within the intracranial arterial structures, the rupture of which accounts for high mortality and morbidity rates. Current clinical protocols require radiologists to manually annotate IAs on Magnetic Resonance Angiography (MRA) images, which is inherently subjective and time‐consuming. Given these limitations, there is an urgent need to explore methods for automated and accurate segmentation of IAs from MRA images. In particular, recent years have witnessed the proliferation of sophisticated computational techniques, with deep learning algorithms—especially the 3D U‐Net and its derivatives—gaining prominence in segmentation works. Nevertheless, convolutional neural network (CNN)‐based models have an inherent limitation in capturing long‐range spatial dependencies, which inadvertently compromises the retention of global features critical for segmentation. In response to this challenge, we introduce an avant‐garde architectural design, dubbed staged cluster transformers (SCTR), which incorporates cluster mechanism into vision transformers to perform volumetric MRA image segmentation. In addition to the MRA clustering branch, the spatially aligned brain Magnetic Resonance Imaging (MRI) representation branch is also combined to extract the structural features and assist the network in learning richer contextual and boundary information for accurate voxel prediction. For validation, we utilized both a publicly available challenge dataset and an internal clinical dataset in this study. Our proposed model achieves dice similarity coefficients (DSC) of 0.5587 and 0.8110 on these two datasets, respectively, outperforming other state‐of‐the‐art approaches. The results suggest that SCTR is a promising method for automatic segmentation of IAs. Our code is available at https://github.com/guolilin/SCTR. [ABSTRACT FROM AUTHOR]

Medical imaging serves as a crucial tool in current cancer diagnosis. However, the quality of medical images is often compromised to minimize the potential risks associated with patient image acquisition. Computer-aided diagnosis systems have made significant advancements in recent years. These systems utilize computer algorithms to identify abnormal features in medical images, assisting radiologists in improving diagnostic accuracy and achieving consistency in image and disease interpretation. Importantly, the quality of medical images, as the target data, determines the achievable level of performance by artificial intelligence algorithms. However, the pixel value range of medical images differs from that of the digital images typically processed via artificial intelligence algorithms, and blindly incorporating such data for training can result in suboptimal algorithm performance. In this study, we propose a medical image-enhancement scheme that integrates generic digital image processing and medical image processing modules. This scheme aims to enhance medical image data by endowing them with high-contrast and smooth characteristics. We conducted experimental testing to demonstrate the effectiveness of this scheme in improving the performance of a medical image segmentation algorithm. [ABSTRACT FROM AUTHOR]

Background and objective: Recent developments in deep learning have demonstrated impressive performance in accurately identifying individual diseases from chest X-rays (CXRs). However, multiple diseases, stability of the deep network, and class imbalance problems were not addressed with high accuracy for disease detection and classification. So, the main purpose of this work is to develop a fully automatic computer method to detect thirteen types of thoracic disease from CXRs with high accuracy. Methods: In this research, a CX-Ultranet has been proposed for the classification and detection of 13 different thoracic disorders from plain radiographic images. The baseline model employed is EfficientNet, and a multiclass cross-entropy loss function is utilized within a compound scaling structure. Channel shuffling is implemented at various stages of the network, creating reduction cells and incorporating more skip connections. The loss function algorithm and Adam optimizers work synergistically to stabilize the model and facilitate continuous learning from new data over time. Results: The CX-Ultranet demonstrates an average prediction accuracy of 88% when applied to diverse CXR datasets. In comparison to existing state-of-the-art techniques, the CX-Ultranet exhibits a remarkable improvement ranging from 5% to 15%. Additionally, it shows a reduction in operational time by approximately 30% compared to current cutting-edge models under similar environmental and data conditions. Conclusion: The proposed CX-Ultranet achieves superior overall accuracy and effectively addresses imbalanced classes within the dataset. Furthermore, it significantly reduces the duration of network training in relation to FLOPS, thereby establishing a novel benchmark in the field of CXR-based disease diagnosis. [ABSTRACT FROM AUTHOR]

With the rapid growth in communication and computing technologies, the transmission of medical images over the Internet is on the rise. In such a scenario, there is a special need to meet the security and privacy issues and challenges of individual and Intellectual Property (IP) owners. It is highly important for an individual to keep his/her personal medical images against invalid manipulation by impostors. Hence developments of authentication and tamper detection techniques are the need of the hour. For this, a tamper detection and self-recovery watermarking scheme for medical images based on texture degree and cross-embedding is proposed in this paper. Firstly, divide medical images into ROI (Region of Interest) and RONI (Region of Non-Interest); generate a double authentication watermark in ROI to improve the accuracy of tamper detection and reduce the probability of false alarm; calculate texture complexity based on 4-dimensional features in ROI, and divide ROI into texture blocks and smooth blocks; generate different recovery watermarks according to the characteristics of different blocks using compression-aware technology. Then, hide the recovery watermark in RONI based on the reference matrix and cross-embedding technology. Finally, locate the tampered blocks in the ROI based on three level tamper detection strategy including pixel-level, block-level, and multi-direction subband-level; restore the tampered region by the extracted recovery watermark. The experimental results indicate that the tamper detection accuracy of the ROI region is close to 100%. Additionally, at an embedding rate of 1.4074bpp, the PSNR reaches 45.0217 dB and the NC is 0.99. In addition, the scheme provides promising results against copy-paste attacks, collage attacks and steganalysis. Also, the scheme achieves privacy protection. This clearly demonstrates that the proposed scheme has several advantages, including strong tamper detection capability, effective self-recovery, high security, excellent concealment, and robustness. [ABSTRACT FROM AUTHOR]

Accurate automatic segmentation methods for liver and liver tumors are important in helping physicians to diagnose, treat, and observe liver cancer in the postoperative period. Due to the intrinsic locality of convolution, existing convolution-based methods are difficult to establish long-range dependencies. Transformer's cascading attention mechanism can establish global information association but will destroy local details. Based on this, a feature modeling method that fuses convolution and Transformer is proposed. The method interactively fuses local and global representations by mixed embedding to maximize the global dependencies at different resolutions. Meanwhile, the contextual information from different encoding stages is captured by multi-level feature fusion module at the skip connection to obtain richer semantic information. Finally, in order to cope with the variation of liver tumors in size and shape, a deformable multi-scale module is used to extract multi-scale features of tumors. The experiments mainly use Dice similarity coefficient (DSC) as evaluation metrics. The DSCs of liver and tumor on the LiTS17 dataset are 0.920 and 0.748, respectively, and the results show that the proposed network has more accurate liver tumor segmentation results compared to the baseline. [ABSTRACT FROM AUTHOR]

Deep learning recently achieved advancement in the segmentation of medical images. In this regard, U-Net is the most predominant deep neural network, and its architecture is the most prevalent in the medical imaging society. Experiments conducted on difficult datasets directed us to the conclusion that the traditional U-Net framework appears to be deficient in certain respects, despite its overall excellence in segmenting multimodal medical images. Therefore, we propose several modifications to the existing cutting-edge U-Net model. The technical approach involves applying a Multi-Dimensional U-Convolutional Neural Network to achieve accurate segmentation of multimodal biomedical images, enhancing precision and comprehensiveness in identifying and analyzing structures across diverse imaging modalities. As a result of the enhancements, we propose a novel framework called Multi-Dimensional U-Convolutional Neural Network (MDU-CNN) as a potential successor to the U-Net framework. On a large set of multimodal medical images, we compared our proposed framework, MDU-CNN, to the classical U-Net. There have been small changes in the case of perfect images, and a huge improvement is obtained in the case of difficult images. We tested our model on five distinct datasets, each of which presented unique challenges, and found that it has obtained a better performance of 1.32%, 5.19%, 4.50%, 10.23% and 0.87%, respectively. [ABSTRACT FROM AUTHOR]

Purpose: Convolution operator-based neural networks have shown great success in medical image segmentation over the past decade. The U-shaped network with a codec structure is one of the most widely used models. Transformer, a technology used in natural language processing, can capture long-distance dependencies and has been applied in Vision Transformer to achieve state-of-the-art performance on image classification tasks. Recently, researchers have extended transformer to medical image segmentation tasks, resulting in good models. Methods: This review comprises publications selected through a Web of Science search. We focused on papers published since 2018 that applied the transformer architecture to medical image segmentation. We conducted a systematic analysis of these studies and summarized the results. Results: To better comprehend the benefits of convolutional neural networks and transformers, the construction of the codec and transformer modules is first explained. Second, the medical image segmentation model based on transformer is summarized. The typically used assessment markers for medical image segmentation tasks are then listed. Finally, a large number of medical segmentation datasets are described. Conclusion: Even if there is a pure transformer model without any convolution operator, the sample size of medical picture segmentation still restricts the growth of the transformer, even though it can be relieved by a pretraining model. More often than not, researchers are still designing models using transformer and convolution operators. [ABSTRACT FROM AUTHOR]

Three-dimensional (3D) reconstruction of computed tomography (CT) and magnetic resonance imaging (MRI) images is an important diagnostic method, which is helpful for doctors to clearly recognize the 3D shape of the lesion and make the surgical plan. In the study of medical image reconstruction, most researchers use surface rendering or volume rendering method to construct 3D models from image sequences. The watertightness of the algorithm-reconstructed surface will be affected by the segmentation precision or the thickness of the CT layer. The articular surfaces at femoral ends are often used in biomechanical simulation experiments. The model may not conform to its original shape due to the manual repair of non-watertight surfaces. To solve this problem, a 3D reconstruction method of leg bones based on deep learning is proposed in this paper. By deforming the convex hull of the target, comparing with state-of-the-art methods, our method can stably generate a watertight model with higher reconstruction accuracy. In the situation of target transition structures getting fuzzy and the layer spacing increasing, the proposed method can maintain better reconstruction performance and appear higher robustness. Also, the chamfer loss is optimized based on the rotational shape of the leg bones, and the weight of the loss function can be assigned according to the geometric characteristics of the target. Experiment results show that the optimization method improves the accuracy of the model. Furthermore, our research provides a reference for the application of deep learning in medical image reconstruction. [ABSTRACT FROM AUTHOR]

Medical image registration is a critical preprocessing step in medical image analysis. While traditional medical image registration techniques have matured, their registration speed and accuracy still fall short of clinical requirements. In this paper, we propose an improved VoxelMorph network incorporating ResNet modules and CBAM (RCV-Net), for 3D multimodal unsupervised registration. Unlike popular convolution-based U-shaped registration networks like VoxelMorph, RCV-Net incorporates the convolutional block attention module (CBAM) during the convolution process. This inclusion enhances the feature map information extraction capabilities during training and effectively prevents information loss. Additionally, we introduce a lightweight and residual network module at the network's base, which enhances learning ability without significantly increasing training parameters. To evaluate the superiority of our registration model, we utilize evaluation metrics such as structural similarity (SSIM), peak signal-to-noise ratio (PSNR), and mean square error (MSE). Experimental results demonstrate that our proposed network structure outperforms current state-of-the-art methods, yielding better performance in multimodal registration tasks. Furthermore, generalization testing on databases outside of the training set has confirmed the optimal registration effectiveness of our model. [ABSTRACT FROM AUTHOR]

Background: Digital devices can easily forge medical images. Copy-move forgery detection (CMFD) in medical image has led to abuses in areas where access to advanced medical devices is unavailable. Forgery of the copy-move image directly affects the doctor's decision. The method discussed here is an optimal method for detecting medical image forgery. Methods: The proposed method is based on an evolutionary algorithm that can detect fake blocks well. In the first stage, the image is taken to the signal level with the help of a discrete cosine transform (DCT). It is then ready for segmentation by applying discrete wavelet transform (DWT). The low-low band of DWT, which has the most image properties, is divided into blocks. Each block is searched using the equilibrium optimization algorithm. The blocks are most likely to be selected, and the final image is generated. Results: The proposed method was evaluated based on three criteria of precision, recall, and F1 and obtained 90.07%, 92.34%, and 91.56%, respectively. It is superior to the methods studied on medical images. Conclusions: It concluded that our method for CMFD in the medical images was more accurate. [ABSTRACT FROM AUTHOR]

In the intricate network environment, the secure transmission of medical images faces challenges such as information leakage and malicious tampering, significantly impacting the accuracy of disease diagnoses by medical professionals. To address this problem, the authors propose a robust feature watermarking algorithm for encrypted medical images based on multi‐stage discrete wavelet transform (DWT), Daisy descriptor, and discrete cosine transform (DCT). The algorithm initially encrypts the original medical image through DWT‐DCT and Logistic mapping. Subsequently, a 3‐stage DWT transformation is applied to the encrypted medical image, with the centre point of the LL3 sub‐band within its low‐frequency component serving as the sampling point. The Daisy descriptor matrix for this point is then computed. Finally, a DCT transformation is performed on the Daisy descriptor matrix, and the low‐frequency portion is processed using the perceptual hashing algorithm to generate a 32‐bit binary feature vector for the medical image. This scheme utilises cryptographic knowledge and zero‐watermarking technique to embed watermarks without modifying medical images and can extract the watermark from test images without the original image, which meets the basic requirements of medical image watermarking. The embedding and extraction of watermarks are accomplished in a mere 0.160 and 0.411s, respectively, with minimal computational overhead. Simulation results demonstrate the robustness of the algorithm against both conventional attacks and geometric attacks, with a notable performance in resisting rotation attacks. [ABSTRACT FROM AUTHOR]

With the wide application of digital watermarking technology in the field of medical imaging, the security of medical image information has been improved. Aiming at the problem of poor robustness of medical image watermarking algorithms against geometric attacks, the security of medical images cannot be guaranteed. This paper proposed a zero watermarking algorithm for medical images based on KAZE-DCT. First, KAZE-DCT is used to extract feature vectors of medical images, and perceptual hashing is used to obtain feature sequences of medical images. Then, chaotic mapping is used to encrypt the multi-watermark images, and the zero watermarking technology is applied to embed and extract the watermarks. Finally, the correlation coefficient is used to measure the correlation between the algorithm embedding and extracting the watermarks. Experimental results show that the algorithm can effectively extract watermarks. Moreover, it has good robustness against both common attacks and geometric attacks. [ABSTRACT FROM AUTHOR]