Congenital heart defect,accounting for about 30%of congenital defects,is the most common one.Data shows that congenital heart defects have seriously affected the birth rate of healthy newborns.In Fetal andNeonatal Car...Congenital heart defect,accounting for about 30%of congenital defects,is the most common one.Data shows that congenital heart defects have seriously affected the birth rate of healthy newborns.In Fetal andNeonatal Cardiology,medical imaging technology(2D ultrasonic,MRI)has been proved to be helpful to detect congenital defects of the fetal heart and assists sonographers in prenatal diagnosis.It is a highly complex task to recognize 2D fetal heart ultrasonic standard plane(FHUSP)manually.Compared withmanual identification,automatic identification through artificial intelligence can save a lot of time,ensure the efficiency of diagnosis,and improve the accuracy of diagnosis.In this study,a feature extraction method based on texture features(Local Binary Pattern LBP and Histogram of Oriented Gradient HOG)and combined with Bag of Words(BOW)model is carried out,and then feature fusion is performed.Finally,it adopts Support VectorMachine(SVM)to realize automatic recognition and classification of FHUSP.The data includes 788 standard plane data sets and 448 normal and abnormal plane data sets.Compared with some other methods and the single method model,the classification accuracy of our model has been obviously improved,with the highest accuracy reaching 87.35%.Similarly,we also verify the performance of the model in normal and abnormal planes,and the average accuracy in classifying abnormal and normal planes is 84.92%.The experimental results show that thismethod can effectively classify and predict different FHUSP and can provide certain assistance for sonographers to diagnose fetal congenital heart disease.展开更多
We present a method for detecting oil spills in a complex scene of SAR imagery,including segmenting oil spills,and avoiding false alarms.Segmentation is carried out using a multi-time and multi-hierarchical method by ...We present a method for detecting oil spills in a complex scene of SAR imagery,including segmenting oil spills,and avoiding false alarms.Segmentation is carried out using a multi-time and multi-hierarchical method by dividing the complex sea surface into bright sea and dark sea.Gray-based and edge-based segmentations are done to extract oil spills from bright and dark sea,respectively.The proposed method can extract complete oil spills,obtain better visual results,and increase detection probability more accurately than the traditional method.Based on the surrounding features and the oil spills’features,dark land spots and low contrast dark spots are removed efficiently,thus reducing false alarms.The experimental results demonstrate that the proposed algorithm has fast computation speed,high detection accuracy,and is very useful and effective for detecting oil spills in SAR imagery.展开更多
基金supported by Fujian Provincial Science and Technology Major Project(No.2020HZ02014)by the grants from National Natural Science Foundation of Fujian(2021J01133,2021J011404)by the Quanzhou Scientific and Technological Planning Projects(Nos.2018C113R,2019C028R,2019C029R,2019C076R and 2019C099R).
文摘Congenital heart defect,accounting for about 30%of congenital defects,is the most common one.Data shows that congenital heart defects have seriously affected the birth rate of healthy newborns.In Fetal andNeonatal Cardiology,medical imaging technology(2D ultrasonic,MRI)has been proved to be helpful to detect congenital defects of the fetal heart and assists sonographers in prenatal diagnosis.It is a highly complex task to recognize 2D fetal heart ultrasonic standard plane(FHUSP)manually.Compared withmanual identification,automatic identification through artificial intelligence can save a lot of time,ensure the efficiency of diagnosis,and improve the accuracy of diagnosis.In this study,a feature extraction method based on texture features(Local Binary Pattern LBP and Histogram of Oriented Gradient HOG)and combined with Bag of Words(BOW)model is carried out,and then feature fusion is performed.Finally,it adopts Support VectorMachine(SVM)to realize automatic recognition and classification of FHUSP.The data includes 788 standard plane data sets and 448 normal and abnormal plane data sets.Compared with some other methods and the single method model,the classification accuracy of our model has been obviously improved,with the highest accuracy reaching 87.35%.Similarly,we also verify the performance of the model in normal and abnormal planes,and the average accuracy in classifying abnormal and normal planes is 84.92%.The experimental results show that thismethod can effectively classify and predict different FHUSP and can provide certain assistance for sonographers to diagnose fetal congenital heart disease.
基金supported by the National Natural Science Foundation of China(Grant Nos.61171194,61120106004)"111"Project of China(Grant No.B14010)
文摘We present a method for detecting oil spills in a complex scene of SAR imagery,including segmenting oil spills,and avoiding false alarms.Segmentation is carried out using a multi-time and multi-hierarchical method by dividing the complex sea surface into bright sea and dark sea.Gray-based and edge-based segmentations are done to extract oil spills from bright and dark sea,respectively.The proposed method can extract complete oil spills,obtain better visual results,and increase detection probability more accurately than the traditional method.Based on the surrounding features and the oil spills’features,dark land spots and low contrast dark spots are removed efficiently,thus reducing false alarms.The experimental results demonstrate that the proposed algorithm has fast computation speed,high detection accuracy,and is very useful and effective for detecting oil spills in SAR imagery.
