Application of graph-curvature features in computer-aided diagnosis for histopathological image identification of gastric cancer

Background Histopathology diagnosis is often regarded as the final diagnostic method for malignant tumors;however,it has some drawbacks.This study explored a computer-aided diagnostic method that can be used to identify benign and malignant gastric cancer using histopathological images.Methods The most suitable process was selected through multiple experiments by comparing multiple meth-ods and features for classification.First,the U-net was applied to segment the image.Next,the nucleus was extracted from the segmented image,and the minimum spanning tree(MST)diagram structure that can cap-ture the topological information was drawn.The third step was to extract the graph-curvature features of the histopathological image according to the MST image.Finally,by inputting the graph-curvature features into the classifier,the recognition results for benign or malignant cancer can be obtained.Results During the experiment,we used various methods for comparison.In the image segmentation stage,U-net,watershed algorithm,and Otsu threshold segmentation methods were used.We found that the U-net method,combined with multiple indicators,was the most suitable for segmentation of histopathological images.In the feature extraction stage,in addition to extracting graph-edge and graph-curvature features,several basic im-age features were extracted,including the red,green and blue feature,gray-level co-occurrence matrix feature,histogram of oriented gradient feature,and local binary pattern feature.In the classifier design stage,we exper-imented with various methods,such as support vector machine(SVM),random forest,artificial neural network,K nearest neighbors,VGG-16,and inception-V3.Through comparison and analysis,it was found that classifica-tion results with an accuracy of 98.57%can be obtained by inputting the graph-curvature feature into the SVM classifier.

Forensic Human Image Identification Using Medical Indicators

Diseases not only bring troubles to people’s body functions and mind but also influence the appearances and behaviours of human beings.Similarly,we can analyse the diseases from people’s appearances and behaviours and use the personal medical history for human identification.In this article,medical indicators presented in abnormal changes of human appearances and behaviours caused by physiological or psychological diseases were introduced,and were applied in the field of forensic identification of human images,which we called medical forensic identification of human images(mFIHI).The proposed method analysed the people’s medical signs by studying the appearance and behaviour characteristics depicted in images or videos,and made a comparative examination between the medical indicators of the questioned human images and the corresponding signs or medical history of suspects.Through a conformity and difference analysis on medical indicators and their indicated diseases,it would provide an important information for human identification from images or videos.A case study was carried out to demonstrate and verify the feasibility of the proposed method of mFIHI,and our results showed that it would be important contents and angles for forensic expert manual examination in forensic human image identification.

Identifying Materials of Photographic Images and Photorealistic Computer Generated Graphics Based on Deep CNNs

Currently,some photorealistic computer graphics are very similar to photographic images.Photorealistic computer generated graphics can be forged as photographic images,causing serious security problems.The aim of this work is to use a deep neural network to detect photographic images(PI)versus computer generated graphics(CG).In existing approaches,image feature classification is computationally intensive and fails to achieve realtime analysis.This paper presents an effective approach to automatically identify PI and CG based on deep convolutional neural networks(DCNNs).Compared with some existing methods,the proposed method achieves real-time forensic tasks by deepening the network structure.Experimental results show that this approach can effectively identify PI and CG with average detection accuracy of 98%.

An Early Warning System for Curved Road Based on OV7670 Image Acquisition and STM32

Nowadays,the number of vehicles in China has increased significantly.The increase of the number of vehicles has also led to the increasingly complex traffic situation and the urgent safety measures in need.However,the existing early warning devices such as geomagnetic,ultrasonic and infrared detection have some shortcomings like difficult installation and maintenance.In addition,geomagnetic detection will damage the road surface,while ultrasonic and infrared detection will be greatly affected by the environment.Considering the shortcomings of the existing solutions,this paper puts forward a solution of early warning for vehicle turning meeting based on image acquisition and microcontrollers.This solution combines image acquisition and processing technology,which uses image sensor to perceive traffic condition and image data analysis algorithm to process perceived image,and then utilize LED display screen to issue an early warning.

Automatic fracture–vug identification and extraction from electric imaging logging data based on path morphology

We present a path morphology method to separate total rock pore space into matrix, fractures and vugs and derive their pore structure spectrum. Thus, we can achieve fine pore evaluation in fracture–vug reservoirs based on electric imaging logging data. We automatically identify and extract fracture–vug information from the electric imaging images by adopting a path morphological operator that remains flexible enough to fit rectilinear and slightly curved structures because they are independent of the structuring element shape. The Otsu method was used to extract fracture–vug information from the background noise caused by the matrix. To accommodate the differences in scale and form of the different target regions,including fracture and vug path, operators with different lengths were selected for their recognition and extraction at the corresponding scale. Polynomial and elliptic functions are used to fit the extracted fractures and vugs, respectively, and the fracture–vug parameters are deduced from the fitted edge. Finally, test examples of numerical simulation data and several measured well data have been provided for the verification of the effectiveness and adaptability of the path morphology method in the application of electric imaging logging data processing. This also provides algorithm support for the fine evaluation of fracture–vug reservoirs.

Microseismic event waveform classification using CNN-based transfer learning models

The efficient processing of large amounts of data collected by the microseismic monitoring system(MMS),especially the rapid identification of microseismic events in explosions and noise,is essential for mine disaster prevention.Currently,this work is primarily performed by skilled technicians,which results in severe workloads and inefficiency.In this paper,CNN-based transfer learning combined with computer vision technology was used to achieve automatic recognition and classification of multichannel microseismic signal waveforms.First,data collected by MMS was generated into 6-channel original waveforms based on events.After that,sample data sets of microseismic events,blasts,drillings,and noises were established through manual identification.These datasets were split into training sets and test sets according to a certain proportion,and transfer learning was performed on AlexNet,GoogLeNet,and ResNet50 pre-training network models,respectively.After training and tuning,optimal models were retained and compared with support vector machine classification.Results show that transfer learning models perform well on different test sets.Overall,GoogLeNet performed best,with a recognition accuracy of 99.8%.Finally,the possible effects of the number of training sets and the imbalance of different types of sample data on the accuracy and effectiveness of classification models were discussed.

The On-Vehicle Inspection System Based on Passive UHF RFID

This article introduces a design theory of vehicle-related management in forms of system linkage in a certain close environment.It analyses the technology advantages,working principles,system structures and design solutions of the scene inspection system based on passive UHF RFID technology,which has functions of data capturing,image collection,wireless data transmission and provision of warning alerts.The system enables scene disposal of vehicle-related management in a specific environment,people management in large-scale events and management of important materials.The system has the capability of rapid network connection and scene inspection especially in emergencies and public security affairs,in which advance deployment is normally inefficient.The system has been successfully applied in the vehicle safety monitoring system in the 2010 Shanghai World Expo Park.

Novel method for identifying wheat leaf disease images based on differential amplification convolutional neural network

In this study,a differential amplification convolutional neural network(DACNN)was proposed and used in the identification of wheat leaf disease images with ideal accuracy.The branches added between the deep convolutional layers can amplify small differences between the real output and the expected output,which made the weight updating more sensitive to the light errors return in the backpropagation pass and significantly improved the fitting capability.Firstly,since there is no large-scale wheat leaf disease images dataset at present,the wheat leaf disease dataset was constructed which included eight kinds of wheat leaf images,and five kinds of data augmentation methods were used to expand the dataset.Secondly,DACNN combined four classifiers:Softmax,support vector machine(SVM),K-nearest neighbor(KNN)and Random Forest to evaluate the wheat leaf disease dataset.Finally,the DACNN was compared with the models:LeNet-5,AlexNet,ZFNet and Inception V3.The extensive results demonstrate that DACNN is better than other models.The average recognition accuracy obtained on the wheat leaf disease dataset is 95.18%.

Nonorthogonal object identification based on ghost imaging

Ghost imaging could be used to make a quick identification of orthogonal objects by means of photocurrent correlation measurements. In this paper, we extend the method to identify nonorthogonal objects. In the method, an object is illuminated by one photon from an entangled pair, and the other one is diffracted into a particular direction by a pre-established multiple-exposure hologram in the idler arm. By the correlation measurements, the nonorthogonal object in the signal arm could be discriminated within a very short time. The constraints for the identification of nonorthogonal objects are presented, which show that the nonorthogonal objects can be discriminated when the overlapping portion between any two objects is less than half of all the objects in the set. The numerical simulations further verify the result.