Errors in these steps degrade the performance of subsequent feature-based classifiers, and also increase the computational complexity.
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Therefore, state-of-the-art methods suffer from both accuracy and processing time. Keeping these latter terms in mind, our method can be highlighted as follows:. In conclusion, we have proved that edge map can measure the evidence of pulmonary abnormalities and is able to provide better performance than the reported state-of-the art methods.
Edge map analysis in chest X-rays for automatic pulmonary abnormality screening. You must be logged in to post a comment. Read offline:. Related Articles: Computational topology for biomedical image… Topology concerns the study of fundamental and intrinsic properties of spaces which are invariant to continuous deformations, extrinsic evaluations or changes in magnitude e. Topology… Pathophysiological advantages derived from… It has been over years since Marie Krogh developed the method to measure the transfer of carbon monoxide CO through the alveolar wall. On the other hand, the mutations in RBPs have been discovered to be… Significant effect of polyoxometalates on the… Gold I cluster complexes have attracted much attention owing to their excellent catalytic activity in a broad range of organic reactions.
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Therefore a lexico-syntactical approach is promising, where key terms and their relations are recognized and mapped on a predefined standard terminology ontology. We propose a two-phase algorithm for terminology matching: In the first pass, a local terminology for recognition is derived as close as possible to the terms used in the radiology reports. In the second pass, the local terminology is mapped to a standard terminology. With an effort of about 20 hours work of a radiologist as domain expert and 10 hours for meetings, a local terminology with about attributes and various value patterns was built.
Pneumonia is a common illness in all parts of the world and is considered as a major cause of death among all age groups. This study was performed to derive practical criteria for performing chest radiographs for the evaluation of community-acquired pneumonia CAP. A total of patients with acute respiratory symptoms and positive findings on chest radiograph were evaluated from December to December The subjects were referred to outpatient clinics or emergency departments of Birjand's medical university hospitals, Iran, and were enrolled as positive cases.
A checklist was completed for each patient including their demographic information, clinical signs and symptoms cough, sputum production, dyspnea, chest pain, fever, tachycardia, and tachypnea , abnormal findings in pulmonary auscultation and laboratory findings erythrocyte sedimentation rate, C-reactive protein levels, and white blood cell count.
An equal number of age-matched individuals with acute respiratory symptoms, but insignificant findings on chest radiography, were included as the control group. Finally, the diagnostic values of different findings were compared.
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The data showed that vital signs and physical examination findings are useful screening parameters for predicting chest radiograph findings in outpatient settings. Automatic estimation of heart boundaries and cardiothoracic ratio from chest x-ray images. Recent studies have suggested that also two-dimensional CTR might contain clinical information about the heart function.
However, manual measurement of such indices is both subjective and time consuming. The algorithm has three main steps: 1 model based lung segmentation, 2 estimation of heart boundaries from lung contours, and 3 computation of cardiothoracic indices from the estimated boundaries. We extended a previously employed lung detection algorithm to automatically estimate heart boundaries without using ground truth heart markings.
We used two datasets: a publicly available dataset with images as well as clinical dataset with studies from Geisinger Health System. The models of lung fields are learned from both datasets. The lung regions in a given test image are estimated by registering the learned models to patient CXRs.
Then, heart region is estimated by applying Harris operator on segmented lung fields to detect the corner points corresponding to the heart boundaries.
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- Automated chest X-ray screening: can edge map measure the evidence of pulmonary abnormalities??
The method was tested on clinical CXRs and average error rates of 7. The proposed method outperforms previous CTR estimation methods without using any heart templates. This method can have important clinical implications as it can provide fast and accurate estimate of cardiothoracic indices.
Reading a radiologist's mind: monitoring rising and falling interest levels while scanning chest x-rays. Radiological images constitute a special class of images that are captured or computed specifically for the purpose of diagnosing patients. However, because these are not "natural" images, radiologists must be trained to interpret them through a process called "perceptual learning".click here
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However, because perceptual learning is implicit, experienced radiologists may sometimes find it difficult to explicitly i. As a result, current methods of training can take years before a new radiologist is fully competent to independently interpret medical images.
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We hypothesize that eye tracking technology coupled with multimedia technology can be used to accelerate the process of perceptual training, through a Hebbian learning process. Of course this requires that the training system have information about what regions of an image are important - information that could presumably be solicited from experienced radiologists. However, our previous work has suggested that experienced radiologists are not always aware of those regions of an image that attract their attention, but are not clinically significant - information that is very important to a radiologist in training.
This paper discusses a study in which local entropy computations were done on scan path data, and were found to provide a quantitative measure of the moment-by-moment interest level of radiologists as they scanned chest x-rays. The results also showed a striking contrast between the moment-by-moment deployment of attention between experienced radiologists and radiologists in training.
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Assessment of limited chest x-ray technique in postcardiac surgery management. The objective of this study is to investigate the safety of elimination of chest radiography in the postcardiac surgery Intensive Care Unit ICU. A total of samples were acceptable for analysis which This means a Abolishing routine CXR in the ICUs would not be harmful for the patients, and it can be managed based on their clinical status and other safer imaging techniques. Overlapping anatomical structures may confound the detection of abnormal pathology, including lung nodules, in conventional single-projection chest radiography.
To minimize this fundamental limiting factor, a dedicated digital multi-projection system for chest imaging was recently developed at the Radiology Department of Duke University. We are reporting the design of the multi-projection imaging system and its initial performance in an ongoing clinical trial. The system is capable of acquiring multiple full-field projections of the same patient along both the horizontal and vertical axes at variable speeds and acquisition frame rates.
These images acquired in rapid succession from slightly different angles about the posterior-anterior PA orientation can be correlated to minimize the influence of overlying anatomy. The developed system has been tested for repeatability and motion blur artifacts to investigate its robustness for clinical trials. The effect of tube-motion on the image quality measured in terms of impact on the modulation transfer function MTF was found to be minimal.
The system was deemed clinic-ready and a clinical trial was subsequently launched. The flexibility of image acquisition built into the system provides a unique opportunity to easily modify it for different clinical applications, including tomosynthesis, correlation imaging CI , and stereoscopic imaging. Accurate collimation helps to reduce unnecessary irradiation and improves radiographic image quality, which is especially important in the radiosensitive paediatric population.
Qualitative standards were assessed based on the EC guidelines, as well as the overexposed field size and needlessly irradiated tissue compared to the MinFS. This study presents a new chest radiography quality control tool which allows assessment of field sizes, distances, overexposures and quality parameters based on the EC guidelines.
Utilising this tool, we detected inadequate field sizes, inspiration depths, and patient positioning. Furthermore, some debatable EC guideline aspects were revealed. Anatomy-based transmission factors for technique optimization in portable chest x-ray. Portable x-ray examinations often account for a large percentage of all radiographic examinations. Currently, portable examinations do not employ automatic exposure control AEC.
To aid in the design of a size-specific technique chart, acrylic slabs of various thicknesses are often used to estimate x-ray transmission for patients of various body thicknesses. This approach, while simple, does not account for patient anatomy, tissue heterogeneity, and the attenuation properties of the human body. To better account for these factors, in this work, we determined x-ray transmission factors using computational patient models that are anatomically realistic.
A Monte Carlo program was developed to model a portable x-ray system. Detailed modeling was done of the x-ray spectrum, detector positioning, collimation, and source-to-detector distance. Simulations were performed using 18 computational patient models from the extended cardiac-torso XCAT family 9 males, 9 females; age range: years; weight range: kg.
The ratio of air kerma at the detector with and without a patient model was calculated as the transmission factor. Our study showed that the transmission factor decreased exponentially with increasing patient thickness. These exponential relationships may be used to optimize imaging techniques for patients of various body thicknesses to aid in the design of clinical technique charts.
Visualizing and enhancing a deep learning framework using patients age and gender for chest x-ray image retrieval. We explore the combination of text metadata, such as patients' age and gender, with image-based features, for X-ray chest pathology image retrieval.
We focus on a feature set extracted from a pre-trained deep convolutional network shown in earlier work to achieve state-of-the-art results. Two distance measures are explored: a descriptor-based measure, which computes the distance between image descriptors, and a classification-based measure, which performed by a comparison of the corresponding SVM classification probabilities.