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Microscopy scanners and artificial intelligence (AI) techniques have facilitated remarkable advancements in biomedicine. Incorporating these advancements into clinical practice is, however, hampered by the variety of digital file formats used, which poses a significant challenge for data processing. Open-source and commercial software solutions have attempted to address proprietary formats, but they fall short of providing comprehensive access to vital clinical information beyond image pixel data. The proliferation of competing proprietary formats makes the lack of interoperability even worse. DICOM stands out as a standard that transcends internal image formats via metadata-driven image exchange in this context. DICOM defines imaging workflow information objects for images, patients’ studies, reports, etc. DICOM promises standards-based pathology imaging, but its clinical use is limited. No FDA-approved digital pathology system natively generates DICOM, and only one high-performance whole slide images (WSI) device has been approved for diagnostic use in Asia and Europe. In a recent series of Digital Pathology Connectathons, the interoperability of our solution was demonstrated by integrating DICOM digital pathology imaging, i.e., WSI, into PACs and enabling their visualisation. However, no system that incorporates state-of-the-art AI methods and directly applies them to DICOM images has been presented. In this paper, we present the first web viewer system that employs WSI DICOM images and AI models. This approach aims to bridge the gap by integrating AI methods with DICOM images in a seamless manner, marking a significant step towards more effective CAD WSI processing tasks. Within this innovative framework, convolutional neural networks, including well-known architectures such as AlexNet and VGG, have been successfully integrated and evaluated.

Today’s technological era, the booming desire for e-healthcare has inflated the attention towards the security of data from cyber attacks. As the digital medical images are transferred over the public network, there is a demand to shield an adequate level of protection. One of the prominent techniques is encryption which secures the medical images. This paper recommends a DICOM image encryption based upon chaotic attractors on frequency domain by integer wavelet transform (IWT) and fused with deoxyribonucleic acid (DNA) sequence on the spatial domain. The proposed algorithm uses a chaotic 3D Lorenz attractor and logistic map to generate pseudo-random keys for encryption. The algorithm involves subsequent stages, i.e. permutation, substitution, encoding, complementary and decoding. To endorse the resistance of the proposed algorithm, various analyses have been examined for 256 × 256 DICOM images by achieving an average entropy of 7.99, larger keyspace of 10238 and non-zero correlation. The overall results confirm that the proposed algorithm is robust against the brute force attacks.

In this growing era, a massive amount of digital electronic health records (EHRs) are transferred through the open network. EHRs are at risk of a myriad of security threats, to overcome such threats, encryption is a reliable technique to secure data. This paper addresses an encryption algorithm based on integer wavelet transform (IWT) blended with deoxyribo nucleic acid (DNA) and chaos to secure the digital medical images. The proposed work comprises of two phases, i.e. a two-stage shuffling phase and diffusion phase. The first stage of shuffling starts with initial block confusion followed by row and column shuffling of pixels as the second stage. The pixels of the shuffled image are circularly shifted bitwise at the first stage of diffusion to enhance the security of the system against differential attack. The second stage of diffusion operation is based on DNA coding and DNA XOR operations. The experimental analyses have been carried out with 100 DICOM test images of 16-bit depth to evaluate the strength of the algorithm against statistical and differential attacks. By the results, the maximum entropy has been obtained an average of 15.79, NPCR of 99.99, UACI of 33.31, and larger keyspace of 10140, which infer that our technique overwhelms various other state-of-the-art techniques.

In today’s tech-driven world, secure communication of medical information is a critical necessity. Protecting the patient’s sensitive medical data through encryption algorithms based on chaos theory has emerged as a prominent research trend. This research proposes a novel 2D-Modified Tinkerbell Map with Henon Map (2D-MTMHM) chaotic equation to generate the pseudo-random key sequences for medical image encryption. Combining the Tinkerbell map with the Henon map exhibits a broader range of chaotic behaviour, making it highly suitable for cryptographic applications. The nature, randomness and sensitivity of the developed 2D-MTMHM equation are validated through the NIST SP800-22 statistical test, bifurcation diagram, Lyapunov exponent, permutation entropy, attractor trajectory, sample entropy and sensitivity test. The generated random key sequences trigger the proposed medical image encryption algorithm, which integrates a shuffling-diffusion process. The shuffling unit of the proposed medical image encryption scheme consists of three distinct phases: row-wise shuffling, column-wise shuffling, and selective shuffling based on cut-off points. The diffusion unit is designed to bit-wise scramble the pixel-shuffled image, further enhancing the randomness and security of the encrypted image. Simulation and experimental analysis demonstrate that the encryption system effectively resists statistical, differential and Brute-force attacks. The algorithm achieves an average entropy of 7.99, a correlation coefficient nearer to zero, a Number of Pixels Change Ratio (NPCR) of 99.6%, and a Unified Average Changing Intensity (UACI) of 33.4%. A larger key space of 10 270 is obtained, implying that the algorithm provides security against brute−force attacks.

This research presents an exploratory study on the integration of additive manufacturing (AM) with medical imaging for the fabrication of anatomical models. The study focuses on the conversion of DICOM (Digital Imaging and Communications in Medicine) data, obtained from computed tomography (CT) scans, into 3D-printable STL files. Rather than developing functional prosthetics, the objective is to create high- fidelity, non-functional anatomical models for use in education, training, and research. Medical imaging data provided by healthcare professionals are processed using specialized segmentation and 3D modeling software to extract and reconstruct targeted anatomical structures. These reconstructions are exported as STL files and fabricated using 3D printing techniques. Key considerations such as image resolution, model scaling, segmentation accuracy, and suitable material selection are addressed to ensure model precision and printability. The proposed workflow establishes a seamless link between diagnostic imaging and physical model creation, demonstrating the potential of AM in medical visualization, pre-surgical planning, and patient communication. This study serves as a proof-of-concept, emphasizing the feasibility and benefits of converting CT-derived imaging data into tangible models. It highlights the growing role of additive manufacturing in healthcare innovation, particularly in improving anatomical comprehension and bridging the gap between digital imaging and physical representation.

With the increasing utilization of medical imaging in clinical practice and the growing dimensions of data volumes generated by various medical imaging modalities, the distribution, storage, and management of digital medical image data sets requires data compression. Over the past few decades, several image compression standards have been proposed by international standardization organizations. This paper discusses the current status of these image compression standards in medical imaging applications together with some of the legal and regulatory issues surrounding the use of compression in medical settings.

Digital Imaging and Communications in Medicine (DICOM) is a trendy for a clinical picture area. The modem tools for the acquisition of pixel has a DICOM interface, which lets in interoperability between gear and the storage in documents of the equal format. However, it is applicable that the pictures and its associated statistics be built-in to the Hospital Information System. The image captured through the capturing devices contains features with higher dimension which increases the time complexity in classification. As the classification performance greatly impacts on the diagnosis of different diseases and supporting the medical practitioner, it is necessary to reduce the features size or dimensionality. Also, by reducing the dimensionality of features, the decisive support system would provide more accurate result to the medical practitioner in making different decisions. The existing techniques suffer to identify the exact features by segmenting the features according to different features. The computerization of a hospital, in a built-in manner, is now not a convenient task. Developing statistics structures that can combine scientific photo records in to the different hospital’s information, except growing interfaces, which can compromise the system’s performance, is critical to add such traits in the facts device itself. Rand index of the image, Global Consistency Error, Variation of Information and PSNR are major scalable parameters in the various segmentation methods. The mentioned parameters are needed to enhance significantly for the better detection of an image through segmentation process. Hence, principle component analysis method and expectancy maximisation based segmentation methods are proposed in this effort.

Many pathologies have started to digitize their glass slides. To ensure long term accessibility, it is desirable to store them in the DICOM format. Currently, many scanners initially store the images in vendor-specific formats and only provide DICOM converters, with only a few producing DICOM directly. However, such a conversion is not lossless for all vendors, and in the case of MRXS files even overlapping tile handling differs. The resulting consequences have not yet been investigated. We converted MRXS files depicting bladder, ovarian and prostate tissue into DICOM images using the 3D Histech/Sysmex converter and an open-source tool both using baseline JPEG for the re-compression. After conversion no human perceptible differences were present between the images, nevertheless they were not identical and had structure similarity indices (SSIM) of ~ 0.85 to ~ 0.96 on average, while the vendor specific converter in general achieved higher values. AI models based on CNNs and current foundation models could distinguish between the original and the converted images in most cases with an accuracy of up to 99.5%. And already trained AI models showed significant performance differences between the image formats in five out of 64 scenarios, mainly when only little data was used during AI training. So, if DICOM images are intended for a diagnostic use, all processes and algorithms must be (re-)evaluated with the converted files, as images are not identical. Nevertheless, the DICOM format is an excellent opportunity to ensure interoperability in future, as some first AI trainings with converted files did not result in systematically decreased performances.

Most medical and health science schools adopt innovative tools to implement the teaching of anatomy to their undergraduate students. The increase in technological resources for educational purposes allows the use of virtual systems in the field of medicine, which can be considered decisive for improving anatomical knowledge, a requisite for safe and competent medical practice. Among these virtual tools, the Anatomage Table 7.0 represents, to date, a pivotal anatomical device for student education and training medical professionals. This review focuses attention on the potential of the Anatomage Table in the anatomical learning process and clinical practice by discussing these topics based on recent publication findings and describing their trends during the COVID-19 pandemic period. The reports documented a great interest in and a positive impact of the use of this technological table by medical students for teaching gross anatomy. Anatomage allows to describe, with accuracy and at high resolution, organ structure, vascularization, and innervation, as well as enables to familiarize with radiological images of real patients by improving knowledge in the radiological and surgical fields. Furthermore, its use can be considered strategic in a pandemic period, since it ensures, through an online platform, the continuation of anatomical and surgical training on dissecting cadavers.