Identification of Chinese Herb Pieces Based on YOLOv4 / 中国实验方剂学杂志

Chinese herbal piece is an important component of the traditional Chinese medicine （TCM） system， and identifying their quality and grading can promote the development and utilization of Chinese herbal pieces. Utilizing deep learning for intelligent identification of Chinese herbal pieces can save time， effort， and cost， while also reasonably avoiding the constraints of human subjectivity， providing a guarantee for efficient identification of Chinese herbal pieces. In this study， a dataset containing 108 kinds of Chinese herbal pieces （14 058 images） was constructed，the basic YOLOv4 algorithm was employed to identify the 108 kinds of Chinese herbal pieces of our database The mean average precision （mAP） of the developed basic YOLOv4 model reached 85.3%. In addition， the receptive field block was introduced into the neck network of YOLOv4 algorithm， and the improved YOLOv4 algorithm was used to identify Chinese herbal pieces. The mAPof the improved YOLOv4 model achieved 88.7%， the average precision of 80 kinds of decoction pieces exceeded 80%， the average precision of 48 kinds of decoction pieces exceeded 90%. These results indicate that adding the receptive field module can help to some extent in the identification of Chinese herbal medicine pieces with different sizes and small volumes. Finally， the average precision of each kind of Chinese herbal medicine piece by the improved YOLOv4 model was further analyzed. Through in-depth analysis of the original images of Chinese herbal medicine pieces with low prediction average precision， it was clarified that the quantity and quality of original images of Chinese herbal medicine pieces are key to performing intelligent object detection. The improved YOLOv4 model constructed in this study can be used for the rapid identification of Chinese herbal pieces， and also provide reference guidance for the manual authentication of Chinese herbal medicine decoction pieces.

Intelligent identification of livestock, a source of Schistosoma japonicum infection, based on deep learning of unmanned aerial vehicle images / 中国血吸虫病防治杂志

Objective To develop an intelligent recognition model based on deep learning algorithms of unmanned aerial vehicle (UAV) images, and to preliminarily explore the value of this model for remote identification, monitoring and management of cattle, a source of Schistosoma japonicum infection. Methods Oncomelania hupensis snail-infested marshlands around the Poyang Lake area were selected as the study area. Image datasets of the study area were captured by aerial photography with UAV and subjected to augmentation. Cattle in the sample database were annotated with the annotation software VGG Image Annotator to create the morphological recognition labels for cattle. A model was created for intelligent recognition of livestock based on deep learning-based Mask R-convolutional neural network (CNN) algorithms. The performance of the model for cattle recognition was evaluated with accuracy, precision, recall, F1 score and mean precision. Results A total of 200 original UAV images were obtained, and 410 images were yielded following data augmentation. A total of 2 860 training samples of cattle recognition were labeled. The created deep learning-based Mask R-CNN model converged following 200 iterations, with an accuracy of 88.01%, precision of 92.33%, recall of 94.06%, F1 score of 93.19%, and mean precision of 92.27%, and the model was effective to detect and segment the morphological features of cattle. Conclusion The deep learning-based Mask R-CNN model is highly accurate for recognition of cattle based on UAV images, which is feasible for remote intelligent recognition, monitoring, and management of the source of S. japonicum infection.

Construction and Application of YOLOv3-Based Diatom Identification Model of Scanning Electron Microscope Images / 法医学杂志

OBJECTIVES@#To construct a YOLOv3-based model for diatom identification in scanning electron microscope images, explore the application performance in practical cases and discuss the advantages of this model.@*METHODS@#A total of 25 000 scanning electron microscopy images were collected at 1 500× as an initial image set, and input into the YOLOv3 network to train the identification model after experts' annotation and image processing. Diatom scanning electron microscopy images of lung, liver and kidney tissues taken from 8 drowning cases were identified by this model under the threshold of 0.4, 0.6 and 0.8 respectively, and were also identified by experts manually. The application performance of this model was evaluated through the recognition speed, recall rate and precision rate.@*RESULTS@#The mean average precision of the model in the validation set and test set was 94.8% and 94.3%, respectively, and the average recall rate was 81.2% and 81.5%, respectively. The recognition speed of the model is more than 9 times faster than that of manual recognition. Under the threshold of 0.4, the mean recall rate and precision rate of diatoms in lung tissues were 89.6% and 87.8%, respectively. The overall recall rate in liver and kidney tissues was 100% and the precision rate was less than 5%. As the threshold increased, the recall rate in all tissues decreased and the precision rate increased. The F1 score of the model in lung tissues decreased with the increase of threshold, while the F1 score in liver and kidney tissues with the increase of threshold.@*CONCLUSIONS@#The YOLOv3-based diatom electron microscope images automatic identification model works at a rapid speed and shows high recall rates in all tissues and high precision rates in lung tissues under an appropriate threshold. The identification model greatly reduces the workload of manual recognition, and has a good application prospect.

Optimal recognition algorithm for solid nuclear track images based on morphology and machine learning / 中国辐射卫生

Objective To propose a computer recognition algorithm for solid nuclear track images based on the machine learning method, and to realize the automatic, fast and accurate recognition of nuclear tracks and improve the efficiency of solid track image analysis. Methods Firstly, 143 images containing tracks were scanned by morphological method to determine the location of suspected tracks, and 1250 material images were captured. 50% of the material were selected as the training set and 30% as the validation set for training the machine learning model. Another 20% of the material were selected as the test set for testing the model recognition result. The algorithm code was written and trained based on the MATLAB software. Results The established solid track recognition algorithm had a strong recognition capability, and the recognition accuracy of the test set could reach 84.8%. The machine learning model program constructed by the algorithm could evolve continuously with the input of training data, further improving the accuracy. Conclusion Based on image morphology and machine learning, the track recognition algorithm was investigated, by which the automatic recognition of solid tracks was better realized. In the future, we will increase the data input of the model, optimize the algorithm, and improve the recognition accuracy, in order to provide a more accurate and efficient method for automatic image track recognition.

Analysis of biolo artificial neural network in prediction of aerobic exercise index based on algorithm / Análise de rede neural artificial na predição do índice de exercício aeróbio baseada em algorítimo / Análisis de red neuronal artificial en la predicción del índice de ejercicio aeróbico

ABSTRACT Objective: To study the relationship between aerobic activity and cardiac autonomic nerve activity by artificial neural network algorithm and biological image fusion; because of the artificial neural network model (ANN) problems, biological image processing technology is introduced based on ANN. Methods: An Ann under biological image intelligence algorithm is proposed, a classifier suitable for electrocardiograph (ECG) screening is designed, and an ECG signal screening system is successfully established. Moreover, the data set of normal recovered ECG signals of the subjects during the experimental period is constructed, and a classifier is used to extract the characteristic data of a normal ECG signal during the experimental period. Results: The changes in resting heart rate and other physical health indicators are analyzed by combining resting physiological indicators, namely heart rate, body weight, body mass index and body fat rate. The results show that the self-designed classifier can efficiently process the ECG images, and long-term regular activities can improve the physical conditions of most people. Most subjects' body weight and body fat rate decrease with the extension of experiment time, and the resting heart rate decreases relatively. Conclusions: Certain indicators can be used to predict a person's dynamic physical health, which indicates that the experimental research of index prediction in this research has a good effect, which not only extends the application of artificial neural network but also lays a foundation for the research and implementation of ECG intelligent testing wearable devices. Level of evidence II; Therapeutic studies - investigation of treatment results.

RESUMO Objetivo: Com o objetivo de estudar a relação entre atividade aeróbia e atividade nervosa autonômica cardíaca por algoritmo de rede neural artificial e fusão biológica de imagens, tendo em vista os problemas existentes no modelo de rede neural artificial (RNA), é introduzida a tecnologia de processamento biológico de imagens com base em ANN. Métodos: um algoritmo de inteligência biológica de imagem Ann é proposto, um classificador adequado para triagem eletrocardiográfica (ECG) é projetado e um sistema de triagem de sinal de ECG é estabelecido com sucesso. Além disso, o conjunto de dados de sinais de ECG normais recuperados dos sujeitos durante o período experimental é construído e um classificador é usado para extrair os dados característicos de um sinal de ECG normal durante o período experimental. Resultados: As alterações na frequência cardíaca em repouso e outros indicadores de saúde física são analisadas pela combinação de indicadores fisiológicos de repouso, a saber, frequência cardíaca, peso corporal, índice de massa corporal e índice de gordura corporal. Os resultados mostram que o classificador autodesenhado pode processar com eficiência as imagens de ECG, e as atividades regulares de longo prazo podem melhorar as condições físicas da maioria das pessoas. O peso corporal e a taxa de gordura corporal da maioria dos indivíduos diminuem com a extensão do tempo do experimento, e a freqüência cardíaca em repouso diminui relativamente. Conclusões: Certos indicadores podem ser usados para prever a saúde física dinâmica de uma pessoa, o que indica que a pesquisa experimental de predição de índice nesta pesquisa tem um bom efeito, que não apenas estende a aplicação da rede neural artificial, mas também estabelece uma base para a pesquisa e implementação de dispositivos vestíveis de teste inteligente de ECG. Nível de evidência II; Estudos terapêuticos- investigação dos resultados do tratamento.

RESUMEN Objetivo: Para estudiar la relación entre la actividad aeróbica y la actividad del nervio autónomo cardíaco mediante el algoritmo de red neuronal artificial y la fusión de imágenes biológicas, ante los problemas existentes en el modelo de red neuronal artificial (ANN), se introduce la tecnología de procesamiento de imágenes biológicas basada en ANA. Métodos: Se propone un algoritmo de inteligencia de imagen biológica de Ann, se diseña un clasificador adecuado para el cribado electrocardiógrafo (ECG) y se establece con éxito un sistema de cribado de señales de ECG. Además, se construye el conjunto de datos de las señales de ECG recuperadas normales de los sujetos durante el período experimental, y se utiliza un clasificador para extraer los datos característicos de una señal de ECG normal durante el período experimental. Resultados: Los cambios en la frecuencia cardíaca en reposo y otros indicadores de salud física se analizan combinando indicadores fisiológicos en reposo, a saber, frecuencia cardíaca, peso corporal, índice de masa corporal y tasa de grasa corporal. Los resultados muestran que el clasificador de diseño propio puede procesar de manera eficiente las imágenes de ECG, y las actividades regulares a largo plazo pueden mejorar las condiciones físicas de la mayoría de las personas. El peso corporal y la tasa de grasa corporal de la mayoría de los sujetos disminuyen con la extensión del tiempo del experimento, y la frecuencia cardíaca en reposo disminuye relativamente. Conclusiones: Ciertos indicadores pueden usarse para predecir la salud física dinámica de una persona, lo que indica que la investigación experimental de predicción de índices en esta investigación tiene un buen efecto, lo que no solo extiende la aplicación de la red neuronal artificial sino que también sienta las bases para la investigación. e implementación de dispositivos portátiles de prueba inteligente de ECG. Nivel de evidencia II; Estudios terapéuticos- investigación de los resultados del tratamiento.

Intelligent Identification of Fritillariae Cirrhosae Bulbus，Crataegi Fructus and Pinelliae Rhizoma Based on Deep Learning Algorithms / 中国实验方剂学杂志

Objective:To propose a new method for detecting and evaluating traditional Chinese medicine （TCM） by artificial intelligence and machine vision technology. Method:Taking Fritillariae Cirrhosae Bulbus， Crataegi Fructus and Pinelliae Rhizoma as the research objects， big data of pictures was collected by machine vision and the image database was established. Through the intelligent analysis of the external characteristics of TCM， the deep convolutional neural network model was established to realize the functions of location detection and variety identification by means of deep learning， so as to significantly improve the accuracy of rapid identification of TCM. Result:The classification accuracy of 11 kinds of Chinese herbal pieces （raw， fried， parched and charred products of Crataegi Fructus， Pinelliae Rhizoma， Pinelliae Rhizoma Praeparatum Cum Zingibere et Alumine， Pinelliae Rhizoma Praeparatum， Pinelliae Rhizoma Praeparatum Cum Alumine and three products of Fritillariae Cirrhosae Bulbus） could be more than 99%， and the average recognition accuracy of specific categories could reach more than 97%. Conclusion:The intelligent identification technology of TCM decoction pieces realized by deep learning algorithms has the advantages of simplicity， rapidity， high precision and quantifiable detection， which can provide technical support for the quality detection and evaluation of TCM， and enrich the research ideas of quality evaluation of TCM.

Advances in the application of machine learning in maxillofacial cysts and tumors / 华西口腔医学杂志

The application of artificial intelligence in medicine has gradually received attention along with its development. Many studies have shown that machine learning has a wide range of applications in stomatology, especially in the clinical diagnosis and treatment of maxillofacial cysts and tumors. This article reviews the application of machine learning in maxillofacial cyst and tumor to provide a new method for the diagnosis of oral and maxillofacial diseases.

Application Progress of Artificial Intelligence Technology in Image Recognition of Capsule Endoscopy / 胃肠病学

Capsule endoscopy (CE) is the main method to detect small intestinal lesions. However, a single CE examination produces about 60 000 images, to screen lesion from the huge amount of images is a time-consuming, boring work, and is easy to cause missed diagnosis because of the limited experience and professional skill of physician. Therefore, it is urgent to develop a system that can automatically detect intestinal lesions. In recent years, the technique of artificial intelligence (AI) has gradually penetrated into the medical field, and the computer-aided diagnostic technology based on big data and cloud computing has become a hot spot of clinical research. The deep learning (DL) model represented by convolutional neural network (CNN) has the ability of rapid recognition of lesions and can effectively reduce the missed diagnosis rate. This article reviewed the application progress of AI technology in image recognition of CE.

Comparison of Three CNN Models Applied in Bone Age Assessment of Pelvic Radiographs of Adolescents / 法医学杂志

Objective To compare the performance of three deep-learning models （VGG19, Inception-V3 and Inception-ResNet-V2） in automatic bone age assessment based on pelvic X-ray radiographs. Methods A total of 962 pelvic X ray radiographs taken from adolescents （481 males, 481 females） aged from 11.0 to 21.0 years in five provinces and cities of China were collected, preprocessed and used as objects of study. Eighty percent of these X ray radiographs were divided into training set and validation set with random sampling method and used for model fitting and hyper-parameters adjustment. Twenty percent were used as test sets, to evaluate the ability of model generalization. The performances of the three models were assessed by comparing the root mean square error （RMSE）, mean absolute error （MAE） and Bland-Altman plots between the model estimates and the chronological ages. Results The mean RMSE and MAE between bone age estimates of the VGG19 model and the chronological ages were 1.29 and 1.02 years, respectively. The mean RMSE and MAE between bone age estimates of the Inception-V3 model and the chronological ages were 1.17 and 0.82 years, respectively. The mean RMSE and MAE between bone age estimates of the Inception-ResNet-V2 model and the chronological ages were 1.11 and 0.84 years, respectively. The Bland-Altman plots showed that the mean value of differences between bone age estimates of Inception-ResNet-V2 model and the chronological ages was the lowest. Conclusion In the automatic bone age assessment of adolescent pelvis, the Inception-ResNet-V2 model performs the best while the Inception-V3 model achieves a similar accuracy as VGG19 model.

Construction of gastroscopic image recognition model based on transfer learning and its application in gastric cancer diagnosis / 第二军医大学学报

Objective To construct a gastroscopic image recognition model based on transfer learning and to explore its diagnostic value for gastric cancer. Methods The clear white-light gastroscopic images from 2 001 gastric cancer patients, 2 119 gastric ulcer patients and 2 168 chronic gastritis patients were collected. All these images were divided into training set image group (1 851 gastric cancer, 1 969 gastric ulcer, and 2 018 chronic gastritis) and testing set image group (150 gastric cancer, 150 gastric ulcer, and 150 chronic gastritis). Champion models VGG19, ResNet50 and Inception-V3 in ImageNet Large-Scale Visual Recognition Challenge (ILSVRC) competition were used as pre-trained models. These models were revised for model training. The training set images were assigned to train the above 3 models, and the testing set images were assigned to validate the models. The whole training process was divided into 2 steps (pre-training and fine-tuning). Results It was found that ResNet50 ranked No.1 in terms of testing accuracy. Its diagnostic accuracy for gastric cancer, gastric ulcer and chronic gastritis reached 93%, 92% and 88%, respectively. Conclusion Based on transfer learning, the gastroscopic image recognition software model constructed by ResNet50 model can more accurately differentiate gastric cancer from benign gastric diseases (gastric ulcer and chronic gastritis).

USM ExerHunt: A Building Image Recognition-Based Exergame

@#Introduction: Exergames is defined as a technology-driven physical activity, which is an innovative way of physical activity that integrates interactive gameplay in the exercise process. The exergames may provide enjoyable experiences that could motivate people to participate and continue playing the game play, while also exercising at the same time. Methods: This article presents a treasure hunt-based walking exergames on android platform with the implementation of intelligence-based image recognition. The exergame, termed USM ExerHunt uses images of Universiti Sains Malaysia buildings as the hints. The participant of the game supposes to find a building shown in the hint, and once reaching the destination captures the image of the building. Then, the application will calculate the total steps taken and calories burnt by the participant using an implementation of accelerometer from the mobile phone. Results: The developed USM ExerHunt application is able to achieve accurate image recognition of USM building, with the accuracy rate of 92%. Besides that, the application is capable of calculating the number of total steps and calories burnt after an exercise routine is completed. Conclusion: This android application has shown a proof of concept in incorporating machine intelligence into an exergame application, with pilot study within the USM community.

Artificial intelligence in medicine / 中华皮肤科杂志

With the development of medical technology,a new era of "big data" and "precision medicine" is coming.Artificial intelligence can assist in the diagnosis and treatment process,and reduce the pressure of data analysis on physicians.At present,artificial intelligence is mainly applied to image recognition,genetics and genomics,intelligent diagnosis and treatment,and prognosis prediction in medicine,whose accuracy may approach human experts.Image recognition is most studied,including skin image recognition.In non-image recognition field,artificial intelligence is less studied in dermatology.In the future,artificial intelligence will increase the efficiency of diagnosis and treatment,so as to benefit both physicians and patients.

Application of artificial intelligence technology in plastic surgery / 中华整形外科杂志

Artificial intelligence (AI) technology has advanced rapidly in the field of medicine in recent five years. This paper summarizes the progress of the research and application of AI in plastic surgery in the era of big data and describes the differences from the previous scientific research. Next, it discusses the main problems existing and the ways to carry out AI research. Finally it proposes to unify the data standard through the deep integration of medical and engineering professionals, establish big data centers, and form strategic cooperative relationships with AI research enterprises to carry out long-term research. At the same time, it is crucial to formulate strategies and method for extensive scientific research collaboration. The paper calls for the clinical application of the AI research result to establish a virtuous circle. Applying artificial intelligence in the field of plastic surgery ultimately promotes the development of the discipline.

Automated Assessment for Bone Age of Left Wrist Joint in Uyghur Teenagers by Deep Learning / 法医学杂志

Objective To realize the automated bone age assessment by applying deep learning to digital radiography(DR)image recognition of left wrist joint in Uyghur teenagers, and explore its practical ap-plication value in forensic medicine bone age assessment. Methods The X-ray films of left wrist joint after pretreatment, which were taken from 245 male and 227 female Uyghur nationality teenagers in Uygur Autonomous Region aged from 13.0 to 19.0 years old, were chosen as subjects. And AlexNet was as a regression model of image recognition. From the total samples above, 60% of male and fe-male DR images of left wrist joint were selected as net train set, and 10% of samples were selected as validation set. As test set, the rest 30%were used to obtain the image recognition accuracy with an error range in ±1.0 and ±0.7 age respectively, compared to the real age. Results The modelling results of deep learning algorithm showed that when the error range was in ±1.0 and ±0.7 age respectively, the accuracy of the net train set was 81.4% and 75.6% in male, and 80.5% and 74.8% in female, respectively. When the error range was in ±1.0 and ±0.7 age respectively, the accuracy of the test set was 79.5% and 71.2% in male, and 79.4% and 66.2% in female, respectively. Conclusion The combination of bone age research on teenagers' left wrist joint and deep learning, which has high accuracy and good feasi-bility, can be the research basis of bone age automatic assessment system for the rest joints of body.

Intelligent medical image recognition: Progress and prospect / 第二军医大学学报

Based on artificial intelligence technology, the intelligent medical image recognition refers to the analysis and process of medical images scanned by medical imaging technologies such as X-ray films, computed tomography and magnetic resonance imaging, and surgical video. Major trends in intelligent medical image recognition include intelligent image diagnosis, three-dimensional reconstruction and registration, intelligent surgery video parsing and so on. Intelligent image diagnosis and three-dimensional reconstruction and registration can improve the efficiency and quality of image recognition, and provide a helpful method for clinical diagnosis and treatment; intelligent surgery video parsing can help surgeons learn and understand surgical procedures, and further guide the operation process. Now the research of intelligent medical image recognition has gained some theoretical and technological achievement and gradually been applied in clinic. In this paper, we summarized the progress of intelligent medical image recognition and put forward the development prospect in this field.

Application of image recognition technology in census of national traditional Chinese medicine resources / 中国中药杂志

With the development of computer and image processing technology, image recognition technology has been applied to the national medicine resources census work at all stages.Among them: ①In the preparatory work, in order to establish a unified library of traditional Chinese medicine resources, using text recognition technology based on paper materials, be the assistant in the digitalization of various categories related to Chinese medicine resources; to determine the representative area and plots of the survey from each census team, based on the satellite remote sensing image and vegetation map and other basic data, using remote sensing image classification and other technical methods to assist in determining the key investigation area. ②In the process of field investigation, to obtain the planting area of Chinese herbal medicine was accurately, we use the decision tree model, spectral feature and object-oriented method were used to assist the regional identification and area estimation of Chinese medicinal materials.③In the process of finishing in the industry, in order to be able to relatively accurately determine the type of Chinese medicine resources in the region, based on the individual photos of the plant, the specimens and the name of the use of image recognition techniques, to assist the statistical summary of the types of traditional Chinese medicine resources. ④In the application of the results of transformation, based on the pharmaceutical resources and individual samples of medicinal herbs, the development of Chinese medicine resources to identify APP and authentic herbs 3D display system, assisted the identification of Chinese medicine resources and herbs identification characteristics. The introduction of image recognition technology in the census of Chinese medicine resources, assisting census personnel to carry out related work, not only can reduce the workload of the artificial, improve work efficiency, but also improve the census results of information technology and sharing application ability. With the deepening of the work of Chinese medicine resources census, image recognition technology in the relevant work will also play its unique role.

Advantages and Application Prospects of Deep Learning in Image Recognition and Bone Age Assessment / 法医学杂志

Deep learning and neural network models have been new research directions and hot issues in the fields of machine learning and artificial intelligence in recent years. Deep learning has made a breakthrough in the applications of image and speech recognitions, and also has been extensively used in the fields of face recognition and information retrieval because of its special superiority. Bone X-ray images express different variations in black-white-gray gradations, which have image features of black and white contrasts and level differences. Based on these advantages of deep learning in image recognition, we combine it with the research of bone age assessment to provide basic datum for constructing a forensic automatic system of bone age assessment. This paper reviews the basic concept and network architectures of deep learning, and describes its recent research progress on image recognition in different research fields at home and abroad, and explores its advantages and application prospects in bone age assessment.

Identification of the Images of Chinese Herb Slices with Deep Convolutional Network / 世界科学技术-中医药现代化

It is of great importance that deep learning of computer for the automate identification of the two-dimensional image of Chinese herbal slices is valuable in the application to medicine,production and education.Traditional methods usually extract low-level image features for the identification,but they cannot give robust recognition results under complex backgrounds.Therefore,higher level image representation is necessary in the image identification.A public Chinese herbal medicine database was constructed with 50 common categories and 2,554 images in total,for training and evaluating our recognition model.Then,the sofimax loss function was adopted to train the convolutional neural network model.As a result,the convolutional neural network can achieve the average precision of 70％ under all the 50 medicine herbal classes.In conclusion,convolutional neural network can obtain good results in image identification with complex backgrounds and mutually occluded herbal slices,which has promising potential for future applications.

A standardization model based on image recognition for performance evaluation of an oral scanner

PURPOSE: Accurate information is essential in dentistry. The image information of missing teeth is used in optically based medical equipment in prosthodontic treatment. To evaluate oral scanners, the standardized model was examined from cases of image recognition errors of linear discriminant analysis (LDA), and a model that combines the variables with reference to ISO 12836:2015 was designed. MATERIALS AND METHODS: The basic model was fabricated by applying 4 factors to the tooth profile (chamfer, groove, curve, and square) and the bottom surface. Photo-type and video-type scanners were used to analyze 3D images after image capture. The scans were performed several times according to the prescribed sequence to distinguish the model from the one that did not form, and the results confirmed it to be the best. RESULTS: In the case of the initial basic model, a 3D shape could not be obtained by scanning even if several shots were taken. Subsequently, the recognition rate of the image was improved with every variable factor, and the difference depends on the tooth profile and the pattern of the floor surface. CONCLUSION: Based on the recognition error of the LDA, the recognition rate decreases when the model has a similar pattern. Therefore, to obtain the accurate 3D data, the difference of each class needs to be provided when developing a standardized model.

Ultrasound image evaluation for nuchal translucency based on image recognition / 中华超声影像学杂志

Objective To establish the model and software for quality assessment of fetal nuchal translucency ultrasound image using computer image recognition technology.Methods The proposed approach firstly divided the input image into four sub-image blocks:the nasal bone(NB) area,the nuchal translucency (NT) area,the midbrain area,and the jaw and chest area.For each sub-image block,the algorithm compared the image block with the corresponding area of the standard training image set,and then determined whether the current image block was the qualified one using the the Gabor feature and Bayesian decision.The input ultrasound image was determined to be qualified only if it had four qualified sub-image blocks.Results The difference between our automatic method and the manual screening by experts wasting small,the method obtained Kappa =0.795 and P ＜0.001.Moreover,the efficiency of our method was much higher than the manual screening method.Conclusions Image recognition technology can effectively assist the sonographer to assess the quality of fetal NT of ultrasound image.The proposed approach can reduce the subjectivity and randomness of the manual evaluation of NT image.