We developed the algorithm for preparation of DICOM images for the construction of a three-dimensional model of the bones of the facial skull. The DICOM image-processing algorithm reduced the data ...loss level about the thin bones of the orbit during building a three-dimensional model of the bones of the facial skull from 22–31% to 3–5%. The developed software automatically changes the color of the pixels of the thin bones of the orbit from gray to white. Thin bones of the orbit were expanded over one pixel using the DICOM image-processing algorithm. The analysis of the image processing results by the developed software was carried out using 3D Slicer software.
Medical images are essential in contemporary medicine because they provide practicable entropy, which is used to diagnose medical conditions. It is useful to visualize abnormality in several parts of ...the body. Image segmentation in the medical has an important function in various applications in diagnosis systems. Researchers have become interested in segmentation algorithms as a result of Computed Tomography (CT) and Magnetic Resonance Imaging (MRI). The Region of Interest (ROI) extracts used in medical applications depend heavily on processes including cancer identification, bulk detection, and organ segmentation. Due to its capacity to deal with uncertainty and imprecision, Neutrosophic image processing (NIP) is a significant domain for uncertainty in medical image processing. Its methods in medicine demonstrate their transcendence. In the suggested work, the primary medical domains that NIP can create for image segmentation from DICOM pictures are highlighted. Due to the way it handles uncertain information, it has been found to be a better method.
•Joint DWT watermarking and fuzzy encryption is employed to secure the medical image.•A novel fuzzy composition based diffusion is proposed.•DWT is utilized for watermarking the patient's ...investigation report in DICOM image.•Data compression is achieved through indirect fuzzy logic encoding.
Digital Imaging and Communications in Medicine (DICOM) is one among the significant formats used worldwide for the representation of medical images. Undoubtedly, medical-image security plays a crucial role in telemedicine applications. Merging encryption and watermarking in medical-image protection paves the way for enhancing the authentication and safer transmission over open channels. In this context, the present work on DICOM image encryption has employed a fuzzy chaotic map for encryption and the Discrete Wavelet Transform (DWT) for watermarking. The proposed approach overcomes the limitation of the Arnold transform—one of the most utilised confusion mechanisms in image ciphering. Various metrics have substantiated the effectiveness of the proposed medical-image encryption algorithm.
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Relevance.
An important component of the diagnostic process in combustiology is the collection of anamnesis. At the same time, verification of the very fact of a burn injury of the skin, as a rule, ...does not seem to be a difficult task even for a novice doctor. However, specialists from the I.I. Dzhanelidze Institute regularly encounter errors in the differential diagnosis of burn injuries at the prehospital stage on the part of both ambulance teams (EMS) and surgeons (traumatologists) of non-specialized medical institutions. Each such case attracts attention and takes up a significant part of the time resource of the entire staff of the inpatient department of the emergency medical service for the process of clarifying and verifying the correct diagnosis, as well as determining the further routing of such a patient.
Aim of study.
To study the structure of diagnostic errors at the prehospital stage of the EMS to optimize patient routing by improving the existing organizational and methodological standards.
Material and methods.
A retrospective analysis of the case histories of all victims who were admitted to the inpatient department of the Emergency Medical Department of the I.I. Dzhanelidze St. Petersburg Research Institute of Emergency Medicine during the period from January 2018 to December 2019.
Results.
4,951 patients were admitted with a leading diagnosis of the referring institution, suggesting a history of burn injury. The incidence of diagnostic errors at the prehospital stage of emergency care was 410 cases (8.3%), while burn injury was completely excluded in 178 cases (3.6%).
Conclusions.
1. The results of the analysis revealed a high incidence of diagnostic errors at the prehospital stage of emergency care (8.3%), the main reason for which is the lack of awareness of differential diagnostics within the narrow specialty (combustiology) of primary contact physicians. 2. Shown is the introduction of training practice for doctors and paramedics of emergency medical services, surgeons and traumatologists of primary care in combustiology cycles in specialized burn departments. 3.In order to ensure continuity in the process of providing medical care to patients with burns, it is necessary to create a unified database of convalescents to form a feedback channel with the outpatient clinic during the implementation of the rehabilitation complex.
Background
The use of neuroimaging methods is an integral part of the process of assisting patients with acute cerebrovascular events (ACVE), and computed tomography (CT) is the «gold standard» for ...examining this category of patients. The capabilities of the analysis of CT images may be significantly expanded with modern methods of machine learning including the application of the principles of radiomics. However, since the use of these methods requires large arrays of DICOM (Digital Imaging and Communications in Medicine)-images, their implementation into clinical practice is limited by the lack of representative sample sets. Inaddition, at present, collections (datasets) of CT images of stroke patients, that are suitable for machine learning, are practically not available in the public domain.
Aim of study
Regarding the aforesaid, the aim of this work was to create a DICOM images dataset of native CT and CT-angiography of patients with different types of stroke. Material and meth ods The collection was based on the medical cases of patients hospitalized in the Regional Vascular Center of the N.V. Sklifosovsky Research Institute for Emergency Medicine. We used a previously developed specialized platform to enter clinical data on the stroke cases, to attach CT DICOMimages to each case, to contour 3D areas of interest, and to tag (label) them. A dictionary was developed for tagging, where elements describe the type of lesion, location, and vascular territory.
Results
A dataset of clinical cases and images was formed in the course of the work. It included anonymous information about 220 patients, 130 of them with ischemic stroke, 40 with hemorrhagic stroke, and 50 patients without cerebrovascular disorders. Clinical data included information about type of stroke, presence of concomitant diseases and complications, length of hospital stay, methods of treatment, and outcome. The results of 370 studies of native CT and 102 studies of CT-angiography were entered for all patients. The areas of interest corresponding to direct and indirect signs of stroke were contoured and tagged by radiologists on each series of images.
Conclusion
The resulting collection of images will enable the use of various methods of data analysis and machine learning in solving the most important practical problems including diagnosis of the stroke type, assessment of lesion volume, and prediction of the degree of neurological deficit.
This work provides information concerning the insufficiently studied period in the history of Sheremetev Hospital and Sklifosovsky Institute for Emergency Medicine in 1916–1925. The data of Russian ...historical and medical literature were used, some archival documents were introduced into scientific circulation, which explain the reasons for the significant reduction in the activities of the Sheremetev Hospital and the Hospice House in 1916, the closure of the hospital and the House in 1917, as well as the circumstances of the resumption of the Sheremetev Hospital in 1919, organization of the Moscow City Ambulance Station in 1919 and N.V. Sklifosovsky Institute for Emergency Medicine in 1923.
The healthcare sector has witnessed robust growth in the realm of medical information and its application during the last two decades. Consequently, the universal availability of patient records ...using web-based technology has acquired great significance in providing quality healthcare services on a global scale. With paramount aspects such as medical records confidentiality, it is an important consideration of modern times to secure medical information like CT-scan, X-Ray, and MRI when transmitted across public networks. Combining watermarking and BioHashing in medical image protection provides the means to safely transmit data over the internet and enhance authentication. In this context, we present a BioHashing and watermarking-based technique capable of providing integrity, authenticity, and confidentiality to different medical images. At the sender side, first, the BioHashing method is used to generate the watermark which is then embedded in weber local descriptor excitation component images using discrete cosine transform. The proposed technique meets all the requirements of robustness and imperceptibility. The experimental and comparison results demonstrate that the proposed method is superior to existing methods.
•In this paper, a novel robust medical images information protection method has been introduced.•Medical images are secured using the proposed method even if it is attacked during the transmission process.•The proposed method combine Biohashing with watermarking to make the medical image more secure.•The experimental results suggested the proposed method meets all the requirements of robustness and imperceptibility.
A medical data integration center integrates a large volume of medical images from clinical departments, including X-rays, CT scans, and MRI scans. Ideally, all images should be indexed appropriately ...with standard clinical terms. However, some images have incorrect or missing annotations, which creates challenges in searching and integrating data centrally. To address this issue, accurate and meaningful descriptors are needed for indexing fields, enabling users to efficiently search for desired images and integrate them with international standards.
This paper aims to provide concise annotation for missing or incorrectly indexed fields, incorporating essential instance-level information such as radiology modalities (e.g., X-rays), anatomical regions (e.g., chest), and body orientations (e.g., lateral) using a Deep Learning classification model - ResNet50. To demonstrate the capabilities of our algorithm in generating annotations for indexing fields, we conducted three experiments using two open-source datasets, the ROCO dataset, and the IRMA dataset, along with a custom dataset featuring SNOMED CT labels. While the outcomes of these experiments are satisfactory (Precision of >75%) for less critical tasks and serve as a valuable testing ground for image retrieval, they also underscore the need for further exploration of potential challenges. This essay elaborates on the identified issues and presents well-founded recommendations for refining and advancing our proposed approach.
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.
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