Imaging in Healthcare: A Glance at the Present and a Glimpse Into the Future.

The utilization of artificial intelligence (AI) applications in medical imaging relies heavily on imaging informatics. That is a one-of-a-kind professional who works at the crossroads of clinical radiography, data science, and information technology. Imaging informaticians are becoming crucial players in expanding, assessing, and implementing AI in the medical setting. Teleradiology will continue to be a cost-effective healthcare facility that expands. Vendor neutral archive (VNA) isolates image presentation and storing systems, permitting platforms to develop quickly, and is a repository for organization-wide healthcare image data. Efforts are made to incorporate and integrate diagnostic facilities such as radiography and pathology to fulfill the needs and demands of targeted therapy. Developments in computer-aided medical object identification may alter the environment of patient services. Finally, interpreting and processing distinct complex healthcare data will create a data-rich context where evidence-based care and performance development may be driven.

A basal-level activity of ATR links replication fork surveillance and stress response.

Mammalian cells use diverse pathways to prevent deleterious consequences during DNA replication, yet the mechanism by which cells survey individual replisomes to detect spontaneous replication impediments at the basal level, and their accumulation during replication stress, remain undefined. Here, we used single-molecule localization microscopy coupled with high-order-correlation image-mining algorithms to quantify the composition of individual replisomes in single cells during unperturbed replication and under replicative stress. We identified a basal-level activity of ATR that monitors and regulates the amounts of RPA at forks during normal replication. Replication-stress amplifies the basal activity through the increased volume of ATR-RPA interaction and diffusion-driven enrichment of ATR at forks. This localized crowding of ATR enhances its collision probability, stimulating the activation of its replication-stress response. Finally, we provide a computational model describing how the basal activity of ATR is amplified to produce its canonical replication stress response.

Thoracic CT screening: using routinely detectable COPD information.

Efforts to collect thoracic CT images with standardized quality from individuals undergoing longitudinal lung cancer screening have been highlighted as an important opportunity to increase the yield of crucial clinical information obtainable to advance the public health benefits of lung cancer screening.

PET/CT radiomics in breast cancer: Mind the step.

The aim of the present review was to assess the current status of positron emission tomography/computed tomography (PET/CT) radiomics research in breast cancer, and in particular to analyze the strengths and weaknesses of the published papers in order to identify challenges and suggest possible solutions and future research directions. Various combinations of the terms "breast", "radiomic", "PET", "radiomics", "texture", and "textural" were used for the literature search, extended until 8 July 2019, within the PubMed/MEDLINE database. Twenty-six articles fulfilling the inclusion/exclusion criteria were retrieved in full text and analyzed. The studies had technical and clinical objectives, including diagnosis, biological characterization (correlation with histology, molecular subtypes and IHC marker expression), prediction of response to neoadjuvant chemotherapy, staging, and outcome prediction. We reviewed and discussed the selected investigations following the radiomics workflow steps related to the clinical, technical, analysis, and reporting issues. Most of the current evidence on the clinical role of PET/CT radiomics in breast cancer is at the feasibility level. Harmonized methods in image acquisition, post-processing and features calculation, predictive models and classifiers trained and validated on sufficiently representative datasets, adherence to consensus guidelines, and transparent reporting will give validity and generalizability to the results.

A survey on medical image analysis in diabetic retinopathy.

Diabetic Retinopathy (DR) represents a highly-prevalent complication of diabetes in which individuals suffer from damage to the blood vessels in the retina. The disease manifests itself through lesion presence, starting with microaneurysms, at the nonproliferative stage before being characterized by neovascularization in the proliferative stage. Retinal specialists strive to detect DR early so that the disease can be treated before substantial, irreversible vision loss occurs. The level of DR severity indicates the extent of treatment necessary - vision loss may be preventable by effective diabetes management in mild (early) stages, rather than subjecting the patient to invasive laser surgery. Using artificial intelligence (AI), highly accurate and efficient systems can be developed to help assist medical professionals in screening and diagnosing DR earlier and without the full resources that are available in specialty clinics. In particular, deep learning facilitates diagnosis earlier and with higher sensitivity and specificity. Such systems make decisions based on minimally handcrafted features and pave the way for personalized therapies. Thus, this survey provides a comprehensive description of the current technology used in each step of DR diagnosis. First, it begins with an introduction to the disease and the current technologies and resources available in this space. It proceeds to discuss the frameworks that different teams have used to detect and classify DR. Ultimately, we conclude that deep learning systems offer revolutionary potential to DR identification and prevention of vision loss.

Deep image mining for diabetic retinopathy screening.

Deep learning is quickly becoming the leading methodology for medical image analysis. Given a large medical archive, where each image is associated with a diagnosis, efficient pathology detectors or classifiers can be trained with virtually no expert knowledge about the target pathologies. However, deep learning algorithms, including the popular ConvNets, are black boxes: little is known about the local patterns analyzed by ConvNets to make a decision at the image level. A solution is proposed in this paper to create heatmaps showing which pixels in images play a role in the image-level predictions. In other words, a ConvNet trained for image-level classification can be used to detect lesions as well. A generalization of the backpropagation method is proposed in order to train ConvNets that produce high-quality heatmaps. The proposed solution is applied to diabetic retinopathy (DR) screening in a dataset of almost 90,000 fundus photographs from the 2015 Kaggle Diabetic Retinopathy competition and a private dataset of almost 110,000 photographs (e-ophtha). For the task of detecting referable DR, very good detection performance was achieved: Az=0.954 in Kaggle's dataset and Az=0.949 in e-ophtha. Performance was also evaluated at the image level and at the lesion level in the DiaretDB1 dataset, where four types of lesions are manually segmented: microaneurysms, hemorrhages, exudates and cotton-wool spots. For the task of detecting images containing these four lesion types, the proposed detector, which was trained to detect referable DR, outperforms recent algorithms trained to detect those lesions specifically, with pixel-level supervision. At the lesion level, the proposed detector outperforms heatmap generation algorithms for ConvNets. This detector is part of the Messidor® system for mobile eye pathology screening. Because it does not rely on expert knowledge or manual segmentation for detecting relevant patterns, the proposed solution is a promising image mining tool, which has the potential to discover new biomarkers in images.

Roles of triglyceride and phosphate in atherosclerosis of diabetic hemodialysis patients.

Background: A growing number of patients with End-Stage Renal Disease (ESRD) are undergoing long-term hemodialysis (HD). HD needs a vascular access (VA) and complications of VA account for a sizable proportion of its costs. One of the important cardiovascular diseases (CVD) is atherosclerosis, which is a major cause of premature deaths in the world. So, it is essential to find the risk factors to treat them before they cause an obvious CVD. Methods: We analyzed data from 174 ESRD patients who were candidate for Arterio Venous Fistula (AVF) creation from April 2008 to March 2009 in Hasheminejad Kidney Center by convenient sampling. X-ray images were used and C 4.5 algorithm of data mining techniques revealed the roles of two risk factors for atherosclerosis of diabetic ESRD patients. Pearson coefficient was also used to measure the correlation between the parameters. Results: Diabetic patients had significantly more calcified arteries in their forearm X-ray than other patients (p<0.001). Occurrence of atherosclerotic CVD in diabetic HD patients has an adverse relation compared with the controlled levels of their plasma levels of Triglyceride (TG) and Phosphorus. We found an inverse effect of TG and phosphorus plasma levels on the atherosclerotic involvement of radial and ulnar arteries in diabetic HD patients. We observed that the prevalence of radial and ulnar arteries calcification in these patients is lower when they have higher plasma levels of TG and phosphorous. Conclusion: This study investigates the role of high plasma levels of TG and phosphorous in the development of atherosclerosis in diabetic HD patients. Although many studies showed that hypertriglyceridemia plays a promoting role in the development of CVD, our study also found an inverse effect of plasma levels of TG on the atherosclerotic involvement of radial and ulnar arteries in diabetic patients, and therefore our results support this suspicion that hypertriglyceridemia plays a significant role in developing atherosclerosis.

An evolution of image source camera attribution approaches.

Camera attribution plays an important role in digital image forensics by providing the evidence and distinguishing characteristics of the origin of the digital image. It allows the forensic analyser to find the possible source camera which captured the image under investigation. However, in real-world applications, these approaches have faced many challenges due to the large set of multimedia data publicly available through photo sharing and social network sites, captured with uncontrolled conditions and undergone variety of hardware and software post-processing operations. Moreover, the legal system only accepts the forensic analysis of the digital image evidence if the applied camera attribution techniques are unbiased, reliable, nondestructive and widely accepted by the experts in the field. The aim of this paper is to investigate the evolutionary trend of image source camera attribution approaches from fundamental to practice, in particular, with the application of image processing and data mining techniques. Extracting implicit knowledge from images using intrinsic image artifacts for source camera attribution requires a structured image mining process. In this paper, we attempt to provide an introductory tutorial on the image processing pipeline, to determine the general classification of the features corresponding to different components for source camera attribution. The article also reviews techniques of the source camera attribution more comprehensively in the domain of the image forensics in conjunction with the presentation of classifying ongoing developments within the specified area. The classification of the existing source camera attribution approaches is presented based on the specific parameters, such as colour image processing pipeline, hardware- and software-related artifacts and the methods to extract such artifacts. The more recent source camera attribution approaches, which have not yet gained sufficient attention among image forensics researchers, are also critically analysed and further categorised into four different classes, namely, optical aberrations based, sensor camera fingerprints based, processing statistics based and processing regularities based, to present a classification. Furthermore, this paper aims to investigate the challenging problems, and the proposed strategies of such schemes based on the suggested taxonomy to plot an evolution of the source camera attribution approaches with respect to the subjective optimisation criteria over the last decade. The optimisation criteria were determined based on the strategies proposed to increase the detection accuracy, robustness and computational efficiency of source camera brand, model or device attribution.

Automatic Detection of Pearlite Spheroidization Grade of Steel Using Optical Metallography.

To eliminate the effect of subjective factors during manually determining the pearlite spheroidization grade of steel by analysis of optical metallography images, a novel method combining image mining and artificial neural networks (ANN) is proposed. The four co-occurrence matrices of angular second moment, contrast, correlation, and entropy are adopted to objectively characterize the images. ANN is employed to establish a mathematical model between the four co-occurrence matrices and the corresponding spheroidization grade. Three materials used in coal-fired power plants (ASTM A315-B steel, ASTM A335-P12 steel, and ASTM A355-P11 steel) were selected as the samples to test the validity of our proposed method. The results indicate that the accuracies of the calculated spheroidization grades reach 99.05, 95.46, and 93.63%, respectively. Hence, our newly proposed method is adequate for automatically detecting the pearlite spheroidization grade of steel using optical metallography.

Region quad-tree decomposition based edge detection for medical images.

Edge detection in medical images has generated significant interest in the medical informatics community, especially in recent years. With the advent of imaging technology in biomedical and clinical domains, the growth in medical digital images has exceeded our capacity to analyze and store them for efficient representation and retrieval, especially for data mining applications. Medical decision support applications frequently demand the ability to identify and locate sharp discontinuities in an image for feature extraction and interpretation of image content, which can then be exploited for decision support analysis. However, due to the inherent high dimensional nature of the image content and the presence of ill-defined edges, edge detection using classical procedures is difficult, if not impossible, for sensitive and specific medical informatics-based discovery. In this paper, we propose a new edge detection technique based on the regional recursive hierarchical decomposition using quadtree and post-filtration of edges using a finite difference operator. We show that in medical images of common origin, focal and/or penumbral blurred edges can be characterized by an estimable intensity gradient. This gradient can further be used for dismissing false alarms. A detailed validation and comparison with related works on diabetic retinopathy images and CT scan images show that the proposed approach is efficient and accurate.

The Techniques of Image Mining and Their Applications in Medicine / 中国医学物理学杂志

Objective:As a branch of image processing,image mining is a subject for great concern.With the development of image acquisition and storage,human can gain a lot of useful image information,but lack of effective analytic technique,so it is a focus of image mining that how to obtain useful image information and make full use of image information.So this paper gives an overview of the research and applications of image mining.Method:Firstly,it presents the concept and primary frame of image mining and the major techniques of lower layer image mining and higher layer image mining.Then,it discusses some applications in the biomedical,DNA analysis,data analysis of medical imaging and forensic medicine and so on.Result:Image mining is that the technique which can mine to discover new and valuable knowledge from vast collection of image.Conclusion:This article expounds the related technologies about image mining by our study of the image mining areas integrating the usual methods of the methods.Then,it identifies some applications in medicine and future research directions of image mining.

Lower layer image mining in forensic medicine on the zadeh-x transformation / 重庆医科大学学报

Objective:to offer details as clear as possible to be material evidence for Forensic Medicine from the images which were obtained in bad conditions burying in the strong back and can’t be identified by human vision . Methods:using zadeh-x transformation to mine useful information hiding in the dark back. We implement the zadeh-x transformation by VC++ programming on computer,achieving the lower layer image mining in Forensic medicine pictures whose foreground is different from its background. Results:we have realized the lower layer image mining in pictures burying in black ground,electrophoresis graph,vehicle brand and clothing character. Moreover, we have compared the result with the picture dealing by histogram equipoise. Conclusion:The lower layer image mining in Forensic Medicine has been achieved by VC++ programming in this article. Its result is distinctly better than the result dealing by histogram equipoise. Further more,it can mine some information that histogram equipoise can’t do.