Since the COVID-19 pandemic, telemedicine or non-face-to-face medicine has increased significantly. In practice, various types of medical images are essential to achieve effective telemedicine. Medical image encryption algorithms play an irreplaceable role in the fast and secure transmission and storage of these medical images. However, most of the existing medical image encryption algorithms are full encryption algorithms, which are inefficient and time-consuming, so they are not suitable for emergency medical scenarios. To improve the efficiency of encryption, a small number of works have focused on partial or selective encryption algorithms for medical images, in which different levels of encryption strategies were adopted for different information content regions of medical images. However, these encryption algorithms have inadequate security more or less. In this paper, based on the Logistic map, we designed an improved variable dimension map. Then, an encryption algorithm for medical images was proposed based on it. This algorithm has two modes: (1) full encryption mode and (2) semi-full encryption mode, which can better adapt to different medical scenarios, respectively. In full encryption mode, all pixels of medical images are encrypted by using the confusion-diffusion structure. In semi-full encryption mode, the region of interest of medical images is extracted. The confusion was first adopted to encrypt the region of interest, and then, the diffusion was adopted to encrypt the entire image. In addition, no matter which encryption mode is used, the algorithm provides the function of medical image integrity verification. The proposed algorithm was simulated and analyzed to evaluate its effectiveness. The results show that in semi-full encryption mode, the algorithm has good security performance and lower time consumption; while in full encryption mode, the algorithm has better security performance and is acceptable in time.
Biomarkers are anatomical characteristics or naturally occurring measurable molecules indicating physiological or pathological state of an individual. These biomarkers have the potential to detect or predict diseases at an early stage, which is particularly beneficial in timely management of common complications of radiation therapy done in head and neck cancer treatment regime. Xerostomia is one of the most common oral complaints of radiation therapy. Saliva has an abundance of protein biomarkers; however, those related to post-radiation therapy xerostomia need to be explored further. Textural and imaging-based biomarkers are helpful in predicting xerostomia in such patients. This narrative review provides an account of salivary protein and imaging-based biomarkers of radiation therapy-induced xerostomia in head and neck cancer patients.
Microwave medical imaging (MMI) is experiencing a surge in research interest, with antenna performance emerging as a key area for improvement. This work addresses this need by enhancing the directivity of a compact UWB antenna using a Yagi-Uda-inspired reflector antenna. The proposed reflector-loaded antenna (RLA) exhibited significant gain and directivity improvements compared to a non-directional reference antenna. When analyzed for MMI applications, the RLA showed a maximum increase of 4 dBi in the realized gain and of 14.26 dB in the transmitted field strength within a human breast model. Moreover, it preserved the shape of time-domain input signals with a high correlation factor of 94.86%. To further validate our approach, another non-directional antenna with proven head imaging capabilities was modified with a reflector, achieving similar directivity enhancements. The combined results demonstrate the feasibility of RLAs for improved performance in MMI systems.
This paper discussed security of medical images and reviewed some work done regarding them. A fragile watermarking scheme was then proposed that could detect tamper and subsequently recover the image. Our scheme required a secret key and a public chaotic mixing algorithm to embed and recover a tampered image. The scheme was also resilient to VQ attack. The purposes were to verify the integrity and authenticity of medical images. We used 800 x 600 x 8 bits ultrasound (US) greyscale images in our experiment. We tested our algorithm for up to 50% tampered block and obtained 100% recovery for spread-tampered block.
Matched MeSH terms: Diagnostic Imaging/standards*; Diagnostic Imaging/statistics & numerical data
Despite technological advances in imaging, multiple sclerosis (MS) remains a clinical diagnosis that is supported, but not replaced, by laboratory or imaging findings. However, imaging is essential in the current diagnostic criteria of MS, for prediction of the likelihood of MS for patients with clinically isolated syndromes, correlation with lesion pathology and assessment of treatment outcome. This article gives an overview of imaging in MS with particular emphasis on the role of MRI in various diagnostic imaging criteria. Novel imaging for MS using 3 Tesla field strengths, magnetization transfer imaging, diffusion tensor imaging, magnetic resonance spectroscopy and cell-specific contrast will be reviewed.
This paper presents a negative index metamaterial incorporated UWB antenna with an integration of complementary SRR (split-ring resonator) and CLS (capacitive loaded strip) unit cells for microwave imaging sensor applications. This metamaterial UWB antenna sensor consists of four unit cells along one axis, where each unit cell incorporates a complementary SRR and CLS pair. This integration enables a design layout that allows both a negative value of permittivity and a negative value of permeability simultaneous, resulting in a durable negative index to enhance the antenna sensor performance for microwave imaging sensor applications. The proposed MTM antenna sensor was designed and fabricated on an FR4 substrate having a thickness of 1.6 mm and a dielectric constant of 4.6. The electrical dimensions of this antenna sensor are 0.20 λ × 0.29 λ at a lower frequency of 3.1 GHz. This antenna sensor achieves a 131.5% bandwidth (VSWR < 2) covering the frequency bands from 3.1 GHz to more than 15 GHz with a maximum gain of 6.57 dBi. High fidelity factor and gain, smooth surface-current distribution and nearly omni-directional radiation patterns with low cross-polarization confirm that the proposed negative index UWB antenna is a promising entrant in the field of microwave imaging sensors.
In image steganography, determining the optimum location for embedding the secret message precisely with minimum distortion of the host medium remains a challenging issue. Yet, an effective approach for the selection of the best embedding location with least deformation is far from being achieved. To attain this goal, we propose a novel approach for image steganography with high-performance, where extreme learning machine (ELM) algorithm is modified to create a supervised mathematical model. This ELM is first trained on a part of an image or any host medium before being tested in the regression mode. This allowed us to choose the optimal location for embedding the message with best values of the predicted evaluation metrics. Contrast, homogeneity, and other texture features are used for training on a new metric. Furthermore, the developed ELM is exploited for counter over-fitting while training. The performance of the proposed steganography approach is evaluated by computing the correlation, structural similarity (SSIM) index, fusion matrices, and mean square error (MSE). The modified ELM is found to outperform the existing approaches in terms of imperceptibility. Excellent features of the experimental results demonstrate that the proposed steganographic approach is greatly proficient for preserving the visual information of an image. An improvement in the imperceptibility as much as 28% is achieved compared to the existing state of the art methods.
In part two of "Preparing effective illustrations", the other three categories, viz. photographs, radiological images and diagrams, are discussed. Illustrations provide visual information to supplement the results in a scientific paper, and create a visual impact that can improve the readability of a paper. This article provides some basic guidelines to assist authors in preparing effective photographs, radiological images and diagrams.
The development of rapid, noninvasive diagnostics to detect lung diseases is a great need after the COVID-2019 outbreak. The nanotechnology-based approach has improved imaging and facilitates the early diagnosis of inflammatory lung diseases. The multifunctional properties of nanoprobes enable better spatial-temporal resolution and a high signal-to-noise ratio in imaging. Targeted nanoimaging agents have been used to bind specific tissues in inflammatory lungs for early-stage diagnosis. However, nanobased imaging approaches for inflammatory lung diseases are still in their infancy. This review provides a solution-focused approach to exploring medical imaging technologies and nanoprobes for the detection of inflammatory lung diseases. Prospects for the development of contrast agents for lung disease detection are also discussed.
The radiographer's role in the imaging field is producing the best image to diagnose. Hence, this study is conducted to justify the ability of radiographers in terms of diagnostic performance and visual search patterns during radiographic image interpretation based on their experience. The musculoskeletal radiographic images were chosen as radiographers are expected to perform image interpretation in the red dot system as one of the expanded and extended roles of the radiographer. Sensitivity and specificity in the detection of abnormality are measured. The gaze plot, fixation count and duration are compared between groups of radiographers by using an eye tracker. 19 radiographic images consist of upper and lower extremities are used as stimuli in this study. The result from this study shows no significant difference in terms of sensitivity and specificity with a p-value of 0.818 and 0.146 respectively. For visual search pattern, two images have significant different in term of fixation count (Image 1, p = 0.017; Image 2, p = 0.042) and two images in fixation duration (Image 1, p = 0.001; Image 15, p = 0.021). The gaze plot is not different from an unstructured pattern and less coverage. In conclusion, the experience did not give an influence on the radiographic image interpretation. This may suggest that specific training in areas appropriate to the development of the radiographer could improve the image interpretation.
The Medical imaging service in Malaysia is expanding. The presence of
imaging technologies needs to be supported by homegrown research to optimize their
use. This study investigated the contribution of researches by Malaysian practitioners to
the field of Medical imaging in the Malaysian Citation index (MyCite) database. (Copied from article).
This paper presents a conceptual approach to the integration of Islamic perspectives into a Medical Imaging Curriculum to the concept of Outcome-Based Education (OBE). This work is seen within the context of harmonising Islamic principles to a currently accepted concept in education. Although there have been discussions that question the concept of OBE, this paper contends that the integration can benefit from the practicality aspect of OBE. This can reduce the complexities and fatigue in addressing the integration using an educational approach that is different to that being applied to the human sciences. This paper features the main elements in OBE in the form of Islamic programme educational objectives, Islamic programme outcomes, and Islamic domain learning outcomes. The justification to use domain learning outcomes instead of course learning outcome is given. The teaching and learning strategies, as well as the assessment, are examined through a lens that serves to provide a desirable, practical and holistic model of Islamic integration. It is felt that the currently accepted teaching and assessment methodologies can be adapted for the integration exercise. This work also highlights two often overlooked elements of OBE; teacher and student characteristics. The various terminologies that describe the Islamic teacher characteristics and the differences in student learning styles and preferences are presented. Furthermore, suggestions are made to align the assessment of the integration to various taxonomies of learning, with the aim in evaluating the internalisation of the Islamic essences. This work contents that a holistic approach towards integration of Islamic perspectives into Medical Imaging curriculum can be realised.
Spatial segmentation is a critical procedure in mass spectrometry imaging (MSI)-based biochemical analysis. However, the commonly used unsupervised MSI segmentation methods may lead to inappropriate segmentation results as the MSI data is characterized by high dimensionality and low signal-to-noise ratio. This process can be improved by the incorporation of precise prior knowledge, which is hard to obtain in most cases. In this study, we show that the incorporation of partial or coarse prior knowledge from different sources such as reference images or biological knowledge may also help to improve MSI segmentation results. Here, we propose a novel interactive segmentation strategy for MSI data called iSegMSI, which incorporates prior information in the form of scribble-regularization of the unsupervised model to fine-tune the segmentation results. By using two typical MSI data sets (including a whole-body mouse fetus and human thyroid cancer), the present results demonstrate the effectiveness of the iSegMSI strategy in improving the MSI segmentations. Specifically, the method can be used to subdivide a region into several subregions specified by the user-defined scribbles or to merge several subregions into a single region. Additionally, these fine-tuned results are highly tolerant to the imprecision of the scribbles. Our results suggest that the proposed iSegMSI method may be an effective preprocessing strategy to facilitate the analysis of MSI data.
Despite advances in diagnosis and treatment in recent years, tuberculosis (TB) remains a global health concern. Children are amongst the most vulnerable groups affected by this disease. Although TB primarily involves the lungs and mediastinal lymph nodes, it can affect virtually any organ system of the body. Along with clinical history combined with physical examination and laboratory tests, various medical imaging tools help establish the diagnosis. Medical imaging tests are also helpful for follow-up during therapy, to assess complications and exclude other underlying pathologies. This article aims to discuss the utility, strengths and limitations of medical imaging tools in the evaluation of suspected extrathoracic TB in the pediatric population. Imaging recommendations for the diagnosis will be presented along with practical and evidence-based imaging algorithms to serve as a guide for both radiologists and clinicians.
The amount of acquired radiology imaging studies grows worldwide at a rapid pace. Novel information technology tools for radiologists promise an increase of reporting quality and as well quantity at the same time. Automated text report drafting is one branch of this development. We defined for the present study in total 9 cases of distal radius fracture. Command files structured according to a template of the Radiological Society of North America (RSNA) and to Arbeitsgemeinschaft Osteosynthese (AO) classifiers were given as input to the natural language processing tool ChatGPT. ChatGPT was tasked with drafting an appropriate radiology report. A parameter study (n = 5 iterations) was performed. An overall high appraisal of ChatGPT radiology report quality was obtained in a score card based assessment. ChatGPT demonstrates the capability to adjust output files in response to minor changes in input command files. Existing shortcomings were found in technical terminology and medical interpretation of findings. Text drafting tools might well support work of radiologists in the future. They would allow a radiologist to focus time on the observation of image details and patient pathology. ChatGPT can be considered a substantial step forward towards that aim.
In general, most of the existing convolutional neural network (CNN)-based deep-learning models suffer from spatial-information loss and inadequate feature-representation issues. This is due to their inability to capture multiscale-context information and the exclusion of semantic information throughout the pooling operations. In the early layers of a CNN, the network encodes simple semantic representations, such as edges and corners, while, in the latter part of the CNN, the network encodes more complex semantic features, such as complex geometric shapes. Theoretically, it is better for a CNN to extract features from different levels of semantic representation because tasks such as classification and segmentation work better when both simple and complex feature maps are utilized. Hence, it is also crucial to embed multiscale capability throughout the network so that the various scales of the features can be optimally captured to represent the intended task. Multiscale representation enables the network to fuse low-level and high-level features from a restricted receptive field to enhance the deep-model performance. The main novelty of this review is the comprehensive novel taxonomy of multiscale-deep-learning methods, which includes details of several architectures and their strengths that have been implemented in the existing works. Predominantly, multiscale approaches in deep-learning networks can be classed into two categories: multiscale feature learning and multiscale feature fusion. Multiscale feature learning refers to the method of deriving feature maps by examining kernels over several sizes to collect a larger range of relevant features and predict the input images' spatial mapping. Multiscale feature fusion uses features with different resolutions to find patterns over short and long distances, without a deep network. Additionally, several examples of the techniques are also discussed according to their applications in satellite imagery, medical imaging, agriculture, and industrial and manufacturing systems.
This paper presents an automatic region of interest (ROI) segmentation method for application of watermarking in medical images. The advantage of using this scheme is that the proposed method is robust against different attacks such as median, Wiener, Gaussian, and sharpening filters. In other words, this technique can produce the same result for the ROI before and after these attacks. The proposed algorithm consists of three main parts; suggesting an automatic ROI detection system, evaluating the robustness of the proposed system against numerous attacks, and finally recommending an enhancement part to increase the strength of the composed system against different attacks. Results obtained from the proposed method demonstrated the promising performance of the method.
The next generation of medical information system will integrate multimedia data to assist physicians in clinical decision-making, diagnoses, teaching, and research. This paper describes MIARS (Medical Image Annotation and Retrieval System). MIARS not only provides automatic annotation, but also supports text based as well as image based retrieval strategies, which play important roles in medical training, research, and diagnostics. The system utilizes three trained classifiers, which are trained using training images. The goal of these classifiers is to provide multi-level automatic annotation. Another main purpose of the MIARS system is to study image semantic retrieval strategy by which images can be retrieved according to different levels of annotation.
Autoinflammatory diseases constitute a family of disorders defined by aberrant stimulation of inflammatory pathways without involving antigen-directed autoimmunity. They may be divided into monogenic and polygenic types. Monogenic autoinflammatory syndromes are those with identified genetic mutations, such as familial Mediterranean fever, tumor necrosis factor receptor-associated periodic fever syndrome (TRAPS), mevalonate kinase deficiency or hyperimmunoglobulin D syndrome, cryopyrin-associated periodic fever syndromes (CAPS), pyogenic arthritis pyoderma gangrenosum and acne (PAPA) syndrome, interleukin-10 and interleukin-10 receptor deficiencies, adenosine deaminase 2 deficiency and pediatric sarcoidosis. Those without an identified genetic mutation are known as polygenic and include systemic-onset juvenile idiopathic arthritis, idiopathic recurrent acute pericarditis, Behçet syndrome, chronic recurrent multifocal osteomyelitis and inflammatory bowel disease among others. Autoinflammatory disorders are defined by repeating episodes or persistent fever, rash, serositis, lymphadenopathy, arthritis and increased acute phase reactants, and thus may mimic infections clinically. Most monogenic autoinflammatory syndromes present in childhood. However, because of their infrequency, diverse and nonspecific presentation, and the relatively new genetic recognition, diagnosis is usually delayed. In this article, which is Part 1 of a two-part series, the authors update monogenic autoinflammatory diseases in children with special emphasis on imaging features that may help establish the correct diagnosis.