scholarly journals Combining CT Images and Clinical Features of Four Periods to Predict Whether Patients Have Rectal Cancer

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Yingyin Feng ◽  
Qi Ding ◽  
Chen Meng ◽  
Wenfeng Wang ◽  
Jingjing Zhang ◽  
...  
Keyword(s):  
Rectal Cancer ◽  
Random Forest ◽  
Area Under The Curve ◽  
Image Features ◽  
Learning System ◽  
Image Feature ◽  
Good Prediction ◽  
Ct Image ◽  
Clinical Indicators ◽  
Prediction Result

In this paper, we mainly use random forest and broad learning system (BLS) to predict rectal cancer. A total of 246 participants with computed tomography (CT) image records were enrolled. The total model in the training set (combined with imaging and clinical indicators) has the best prediction result, with the area under the curve (AUC) of 0.999 (95% confidence internal (CI): 0.996–1.000) and the accuracy of 0.990 (95%CI: 0.976–1.000). Model 3, the general model in the test set, has the best prediction result, with the AUC of 0.962 (95%CI: 0.915–1.000) and the accuracy of 0.920 (95%CI: 0.845–0.995). The results of the model using random forest prediction are compared with those using BLS prediction. It can be found that there is no statistical difference between the two results. Our prediction model combined with image features has a good prediction result, and this image feature is the most important among all features. Consequently, we can successfully predict rectal cancer through a combination of the clinical indicators and the comprehensive indicators of CT image characteristics in four different periods (plain scan, vein, artery, and excretion).

scholarly journals CT differential diagnosis of COVID-19 and non-COVID-19 in symptomatic suspects: a practical scoring method

2020 ◽  
Author(s):  
Lin Luo ◽  
Zhendong Luo ◽  
Yizhen Jia ◽  
Cuiping Zhou ◽  
Jianlong He ◽  
...  
Keyword(s):  
Scoring System ◽  
Posterior Part ◽  
Area Under The Curve ◽  
Lower Lobe ◽  
Image Features ◽  
Wall Thickening ◽  
Imaging Features ◽  
Ct Image ◽  
Scoring Method ◽  
Exposure History

Abstract Background Although typical and atypical CT image findings of COVID-19 are reported in current studies, the CT image features of COVID-19 overlap with those of viral pneumonia and other respiratory diseases. Hence, it is difficult to make an exclusive diagnosis.Methods Thirty confirmed cases of COVID-19 and forty-three cases of other aetiology or clinically confirmed non-COVID-19 in a general hospital were included. The clinical data including age, sex, exposure history, laboratory parameters and aetiological diagnosis of all patients were collected. Seven positive signs (posterior part/lower lobe predilection, bilateral involvement, rounded GGO, subpleural bandlike GGO, crazy-paving pattern, peripheral distribution, and GGO +/- consolidation) from significant COVID-19 CT image features and four negative signs (only one lobe involvement, only central distribution, tree-in-bud sign, and bronchial wall thickening) from other non-COVID-19 pneumonia were used. The scoring analysis of CT features was compared between the two groups (COVID-19 and non-COVID-19).Results Older age, symptoms of diarrhoea, exposure history related to Wuhan, and a lower white blood cell and lymphocyte count were significantly suggestive of COVID-19 rather than non-COVID-19 (p<0.05). The receiver operating characteristic (ROC) curve of the combined CT image features analysis revealed that the area under the curve (AUC) of the scoring system was 0.854. These cut-off values yielded a sensitivity of 56.67% and a specificity of 95.35% for a score>4, a sensitivity of 100% and a specificity of 23.26% for a score>0, and a sensitivity of 86.67% and a specificity of 67.44% for a score>2.Conclusions With a simple and practical scoring system based on CT imaging features, we can make a hierarchical diagnosis of COVID-19 and non-COVID-19 with different management suggestions.

scholarly journals Helmet Detection Based on Deep Learning and Random Forest on UAV for Power Construction Safety

2021 ◽  
Vol 25 (1) ◽  
pp. 40-49
Author(s):  
Guobing Yan ◽  
◽  
Qiang Sun ◽  
Jianying Huang ◽  
Yonghong Chen
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Deep Learning ◽  
Random Forest ◽  
Convolutional Neural Network ◽  
Recognition Algorithm ◽  
Image Features ◽  
Image Feature ◽  
Feature Extraction Method ◽  
Power Construction

Image recognition is one of the key technologies for worker’s helmet detection using an unmanned aerial vehicle (UAV). By analyzing the image feature extraction method for workers’ helmet detection based on convolutional neural network (CNN), a double-channel convolutional neural network (DCNN) model is proposed to improve the traditional image processing methods. On the basis of AlexNet model, the image features of the worker can be extracted using two independent CNNs, and the essential image features can be better reflected considering the abstraction degree of the features. Combining a traditional machine learning method and random forest (RF), an intelligent recognition algorithm based on DCNN and RF is proposed for workers’ helmet detection. The experimental results show that deep learning (DL) is closely related to the traditional machine learning methods. Moreover, adding a DL module to the traditional machine learning framework can improve the recognition accuracy.

scholarly journals Feature Extraction for Medical CT Images of Sports Tear Injury

2020 ◽  
Author(s):  
Qi Nie ◽  
Ye-bing Zou ◽  
Jerry Chun-Wei Lin
Keyword(s):  
Feature Extraction ◽  
Auxiliary Information ◽  
Low Frequency ◽  
Frequency Component ◽  
Ct Images ◽  
Image Features ◽  
Image Feature ◽  
Ct Image ◽  
Feature Extraction Algorithm ◽  
Extraction Algorithm

Abstract Analysis of medical CT images directly affects the accuracy of clinical case diagnosis. Therefore, feature extraction problem of medical CT images is extremely important. A feature extraction algorithm for medical CT images of sports tear injury is proposed. First, CT images are decomposed into a low frequency component and a series of high frequency components in different directions by wavelet fast decomposition method. The high- and low-frequency information of CT images is enhanced by wavelet layered multi-directional image enhancement algorithm, and the multi-scale enhancement for medical CT images of sports tear injury is completed. Then, edge of the enhanced CT images is extracted using an image edge extraction algorithm based on extended mathematical morphology. Finally, based on the extracted edge information of CT images, feature extraction for medical CT images of sports tear injury is completed by the NSCT-GLCM based CT image feature extraction algorithm. Research results show that the proposed algorithm effectively extracts CT image features of sports tear injury and provides auxiliary information for doctor diagnosis.

FRFCM clustering segmentation method for medical CT image feature diagnosis

2020 ◽  
pp. 1-9
Author(s):  
Qian He ◽  
Juwei Shao ◽  
Jian Pu ◽  
Minjie Zhou ◽  
M. M ◽  
...  
Keyword(s):  
Fuzzy Set ◽  
Medical Image ◽  
Intuitionistic Fuzzy Set ◽  
Image Features ◽  
Clinical Diagnostics ◽  
Image Feature ◽  
Ct Image ◽  
Clustering Methods ◽  
Segmentation Method ◽  
Clustering Method

Medical image recognition is affected by characteristics such as blur and noise, which cause medical image features that cannot be effectively identified and directly affects clinical diagnostics. In order to improve the diagnostic effect of medical CT image features, based on the FRFCM clustering segmentation method, this study combines the medical CT image feature reality, collects data for traditional clustering method analysis, and sorts out the shortcomings of traditional clustering methods. Simultaneously, this study improves the traditional clustering method by combining medical image feature diagnosis requirements. In addition, this study carried out image data processing through simulation, and designed comparative experiments to analyze the performance of the algorithm. The research shows that the FRFCM combined with the intuitionistic fuzzy set proposed in this paper has greatly improved the noise immunity and segmentation performance compared with the FCM based fuzzy set.

scholarly journals CT differential diagnosis of COVID-19 and non-COVID-19 in symptomatic suspects: a practical scoring method

2020 ◽  
Author(s):  
Lin Luo ◽  
Zhendong Luo ◽  
Yizhen Jia ◽  
Cuiping Zhou ◽  
Jianlong He ◽  
...  
Keyword(s):  
Scoring System ◽  
Posterior Part ◽  
Area Under The Curve ◽  
Lower Lobe ◽  
Image Features ◽  
Wall Thickening ◽  
Imaging Features ◽  
Ct Image ◽  
Scoring Method ◽  
Exposure History

Abstract Background: Although typical and atypical CT image findings of COVID-19 are reported in current studies, the CT image features of COVID-19 overlap with those of viral pneumonia and other respiratory diseases. Hence, it is difficult to make an exclusive diagnosis.Methods : Thirty confirmed cases of COVID-19 and forty-three cases of other aetiology or clinically confirmed non-COVID-19 in a general hospital were included. The clinical data including age, sex, exposure history, laboratory parameters and aetiological diagnosis of all patients were collected. Seven positive signs (posterior part/lower lobe predilection, bilateral involvement, rounded GGO, subpleural bandlike GGO, crazy-paving pattern, peripheral distribution, and GGO +/- consolidation) from significant COVID-19 CT image features and four negative signs (only one lobe involvement, only central distribution, tree-in-bud sign, and bronchial wall thickening) from other non-COVID-19 pneumonia were used. The scoring analysis of CT features was compared between the two groups (COVID-19 and non-COVID-19).Results : Older age, symptoms of diarrhoea, exposure history related to Wuhan, and a lower white blood cell and lymphocyte count were significantly suggestive of COVID-19 rather than non-COVID-19 (p<0.05). The receiver operating characteristic (ROC) curve of the combined CT image features analysis revealed that the area under the curve (AUC) of the scoring system was 0.854. These cut-off values yielded a sensitivity of 56.67% and a specificity of 95.35% for a score>4, a sensitivity of 100% and a specificity of 23.26% for a score>0, and a sensitivity of 86.67% and a specificity of 67.44% for a score>2.Conclusions : With a simple and practical scoring system based on CT imaging features, we can make a hierarchical diagnosis of COVID-19 and non-COVID-19 with different management suggestions.

scholarly journals CNNcon: A Quantitative Imaging Tool for Lung CT Image Feature Analysis

2019 ◽  
Author(s):  
Jason Causey ◽  
Jake Qualls ◽  
Jason H. Moore ◽  
Fred Prior ◽  
Xiuzhen Huang
Keyword(s):  
Image Analysis ◽  
Quantitative Imaging ◽  
Computer Systems ◽  
Lung Cancer Screening ◽  
Image Features ◽  
Image Feature ◽  
Detection Accuracy ◽  
Ct Image ◽  
Imaging Tool ◽  
Lung Ct

AbstractBackgroundLung CT scans are widely used for lung cancer screening and diagnosis. Current research focuses on quantitative analytics (radiomics) to improve screening and detection accuracy. However there are very limited numbers of portable software tools for automatic lung CT image analysis.ResultsHere we build a Docker container, CNNcon, as a quantitative imaging tool for analyzing lung CT image features. CNNcon is developed from our recently published algorithm for nodule analysis, based on convolutional neural networks (CNN). When provided with a list of the centroid coordinates of regions of interest (ROI) in a volumetric CT study containing potential lung nodules, CNNcon can automatically generate highly accurate malignancy prediction of each ROI. CNNcon can also generate a vector of image features of each ROI, to facilitate further analyses by combining image features and other clinical features. As a Docker container, CNNcon is portable to various computer systems, convenient to install, and easy to use. CNNcon was tested on different computer systems and generated identical results.ConclusionsWe anticipate that CNNcon will be a useful tool and broadly acceptable to the research community interested in quantitative image analysis.AvailabilityCNNcon and document are publicly available and can be downloaded from the website: http://bioinformatics.astate.edu/CNN-Container/

High-resolution electron microscopy of the atomic structure of some grain boundaries in Au

1986 ◽  
Vol 44 ◽  
pp. 414-415
Author(s):  
W. Krakow ◽  
D. A. Smith
Keyword(s):  
Atomic Structure ◽  
High Resolution Electron Microscopy ◽  
Image Features ◽  
Image Feature ◽  
Resolution Electron ◽  
Tilt Boundaries ◽  
Processing Techniques ◽  
Atom Position ◽  
Structure Configuration

The successful determination of the atomic structure of [110] tilt boundaries in Au stems from the investigation of microscope performance at intermediate accelerating voltages (200 and 400kV) as well as a detailed understanding of how grain boundary image features depend on dynamical diffraction processes variation with specimen and beam orientations. This success is also facilitated by improving image quality by digital image processing techniques to the point where a structure image is obtained and each atom position is represented by a resolved image feature. Figure 1 shows an example of a low angle (∼10°) Σ = 129/[110] tilt boundary in a ∼250Å Au film, taken under tilted beam brightfield imaging conditions, to illustrate the steps necessary to obtain the atomic structure configuration from the image. The original image of Fig. 1a shows the regular arrangement of strain-field images associated with the cores of ½ [10] primary dislocations which are separated by ∼15Å.

Structural displacement and strain monitoring based on the edge detection operator

2016 ◽  
Vol 20 (2) ◽  
pp. 191-201 ◽  
Author(s):  
Wei Lu ◽  
Yan Cui ◽  
Jun Teng
Keyword(s):  
Edge Detection ◽  
Image Features ◽  
Image Feature ◽  
Monitoring Method ◽  
Strain Monitoring ◽  
Morphological Operator ◽  
Filtering Effect ◽  
Circular Target ◽  
Plane Displacement ◽  
The Cost

To decrease the cost of instrumentation for the strain and displacement monitoring method that uses sensors as well as considers the structural health monitoring challenges in sensor installation, it is necessary to develop a machine vision-based monitoring method. For this method, the most important step is the accurate extraction of the image feature. In this article, the edge detection operator based on multi-scale structure elements and the compound mathematical morphological operator is proposed to provide improved image feature extraction. The proposed method can not only achieve an improved filtering effect and anti-noise ability but can also detect the edge more accurately. Furthermore, the required image features (vertex of a square calibration board and centroid of a circular target) can be accurately extracted using the extracted image edge information. For validation, the monitoring tests for the structural local mean strain and in-plane displacement were designed accordingly. Through analysis of the error between the measured and calculated values of the structural strain and displacement, the feasibility and effectiveness of the proposed edge detection operator are verified.

scholarly journals A Lightweight Fusion Distillation Network for Image Deblurring and Deraining

Sensors ◽  
2021 ◽  
Vol 21 (16) ◽  
pp. 5312
Author(s):  
Yanni Zhang ◽  
Yiming Liu ◽  
Qiang Li ◽  
Jianzhong Wang ◽  
Miao Qi ◽  
...  
Keyword(s):  
Deep Learning ◽  
Image Deblurring ◽  
Image Features ◽  
Image Feature ◽  
Model Complexity ◽  
Small Scale ◽  
Feature Maps ◽  
Learning Framework ◽  
Channel Information ◽  
Scale Spaces

Recently, deep learning-based image deblurring and deraining have been well developed. However, most of these methods fail to distill the useful features. What is more, exploiting the detailed image features in a deep learning framework always requires a mass of parameters, which inevitably makes the network suffer from a high computational burden. We propose a lightweight fusion distillation network (LFDN) for image deblurring and deraining to solve the above problems. The proposed LFDN is designed as an encoder–decoder architecture. In the encoding stage, the image feature is reduced to various small-scale spaces for multi-scale information extraction and fusion without much information loss. Then, a feature distillation normalization block is designed at the beginning of the decoding stage, which enables the network to distill and screen valuable channel information of feature maps continuously. Besides, an information fusion strategy between distillation modules and feature channels is also carried out by the attention mechanism. By fusing different information in the proposed approach, our network can achieve state-of-the-art image deblurring and deraining results with a smaller number of parameters and outperform the existing methods in model complexity.

scholarly journals Comorbidity index for predicting mortality at 6 months after reperfusion therapy

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Hyo Suk Nam ◽  
Young Dae Kim ◽  
Joonsang Yoo ◽  
Hyungjong Park ◽  
Byung Moon Kim ◽  
...  
Keyword(s):  
Prospective Cohort ◽  
Multivariate Logistic Regression Analysis ◽  
Area Under The Curve ◽  
Reperfusion Therapy ◽  
Good Prediction ◽  
Multivariate Logistic Regression ◽  
Stroke Patients ◽  
Number Of Patients ◽  
Comorbidity Index ◽  
Using Data

AbstractThe eligibility of reperfusion therapy has been expanded to increase the number of patients. However, it remains unclear the reperfusion therapy will be beneficial in stroke patients with various comorbidities. We developed a reperfusion comorbidity index for predicting 6-month mortality in patients with acute stroke receiving reperfusion therapy. The 19 comorbidities included in the Charlson comorbidity index were adopted and modified. We developed a statistical model and it was validated using data from a prospective cohort. Among 1026 patients in the retrospective nationwide reperfusion therapy registry, 845 (82.3%) had at least one comorbidity. As the number of comorbidities increased, the likelihood of mortality within 6 months also increased (p < 0.001). Six out of the 19 comorbidities were included for developing the reperfusion comorbidity index on the basis of the odds ratios in the multivariate logistic regression analysis. This index showed good prediction of 6-month mortality in the retrospective cohort (area under the curve [AUC], 0.747; 95% CI, 0.704–0.790) and in 333 patients in the prospective cohort (AUC, 0.784; 95% CI, 0.709–0.859). Consideration of comorbidities might be helpful for the prediction of the 6-month mortality in patients with acute ischemic stroke who receive reperfusion therapy.

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