Objective To automatically segment diabetic retinal exudation features from deep learning color fundus images. Methods An applied study. The method of this study is based on the U-shaped network model of the Indian Diabetic Retinopathy Image Dataset (IDRID) dataset, introduces deep residual convolution into the encoding and decoding stages, which can effectively extract seepage depth features, solve overfitting and feature interference problems, and improve the model's feature expression ability and lightweight performance. In addition, by introducing an improved context extraction module, the model can capture a wider range of feature information, enhance the perception ability of retinal lesions, and perform excellently in capturing small details and blurred edges. Finally, the introduction of convolutional triple attention mechanism allows the model to automatically learn feature weights, focus on important features, and extract useful information from multiple scales. Accuracy, recall, Dice coefficient, accuracy and sensitivity were used to evaluate the ability of the model to detect and segment the automatic retinal exudation features of diabetic patients in color fundus images. Results After applying this method, the accuracy, recall, dice coefficient, accuracy and sensitivity of the improved model on the IDRID dataset reached 81.56%, 99.54%, 69.32%, 65.36% and 78.33%, respectively. Compared with the original model, the accuracy and Dice index of the improved model are increased by 2.35% , 3.35% respectively. Conclusion The segmentation method based on U-shaped network can automatically detect and segment the retinal exudation features of fundus images of diabetic patients, which is of great significance for assisting doctors to diagnose diseases more accurately.
ObjectiveTo observe the changes of macula in patients with high myopia after phacoemulsification. MethodsIn 20 patients with high myopia with ocular axial length≥27 mm, optical coherence tomography (OCT) was performed on the operative and contralateral eyes 1 week before and after monocular phacoemulsification, respectively, and the OCT images of macula of the operative eyes were observed and compared.ResultsOne week before and after phacoemulsification, the mean macular fovea thickness of the patients with high myopia was (131.6±16.37) μm and (189.75±45.69) μm, respectively, with a significant difference (t=2.805, P=0.01). Simultaneously, the mean macular fovea thickness of the contralateral eyes was (133.5±15.12) μm and (133.5±14.63) μm, respectively, with a non-significant difference (t=1.367, P=0.853). In 20 operative eyes 1 week after phacoemulsification, 3 had vitreous strand around the macula with retinal thickening, 1 had retinoschisis in macular area, and 2 had obvious retinal thickening with slight retinal edema.ConclusionRetinal thickening occurs in the patients with high myopia after phacoemulsification. Traction of retina by vitreous strand or subclinical retinoschisis may occur in some patients.(Chin J Ocul Fundus Dis, 2005,21:90-92)
The feature extraction and feature selection are the important issues in pattern recognition. Based on the geometric algebra representation of vector, a new feature extraction method using blade coefficient of geometric algebra was proposed in this study. At the same time, an improved differential evolution (DE) feature selection method was proposed to solve the elevated high dimension issue. The simple linear discriminant analysis was used as the classifier. The result of the 10-fold cross-validation (10 CV) classification of public breast cancer biomedical dataset was more than 96% and proved superior to that of the original features and traditional feature extraction method.
Aiming at the problem that the feature extraction ability of forehead single-channel electroencephalography (EEG) signals is insufficient, which leads to decreased fatigue detection accuracy, a fatigue feature extraction and classification algorithm based on supervised contrastive learning is proposed. Firstly, the raw signals are filtered by empirical modal decomposition to improve the signal-to-noise ratio. Secondly, considering the limitation of the one-dimensional signal in information expression, overlapping sampling is used to transform the signal into a two-dimensional structure, and simultaneously express the short-term and long-term changes of the signal. The feature extraction network is constructed by depthwise separable convolution to accelerate model operation. Finally, the model is globally optimized by combining the supervised contrastive loss and the mean square error loss. Experiments show that the average accuracy of the algorithm for classifying three fatigue states can reach 75.80%, which is greatly improved compared with other advanced algorithms, and the accuracy and feasibility of fatigue detection by single-channel EEG signals are significantly improved. The results provide strong support for the application of single-channel EEG signals, and also provide a new idea for fatigue detection research.
Image feature extraction is an important part of image processing and it is an important field of research and application of image processing technology. Uygur medicine is one of Chinese traditional medicine and researchers pay more attention to it. But large amounts of Uygur medicine data have not been fully utilized. In this study, we extracted the image color histogram feature of herbal and zooid medicine of Xinjiang Uygur. First, we did preprocessing, including image color enhancement, size normalizition and color space transformation. Then we extracted color histogram feature and analyzed them with statistical method. And finally, we evaluated the classification ability of features by Bayes discriminant analysis. Experimental results showed that high accuracy for Uygur medicine image classification was obtained by using color histogram feature. This study would have a certain help for the content-based medical image retrieval for Xinjiang Uygur medicine.
Vascular perfusion distribution in fibroids contrast-enhanced ultrasound images provides useful pathological and physiological information, because the extraction of the vascular perfusion area can be helpful to quantitative evaluation of uterine fibroids blood supply. The pixel gray scale in vascular perfusion area of fibroids contrast-enhanced ultrasound image sequences is different from that in other regions, and, based on this, we proposed a method of extracting vascular perfusion area of fibroids. Firstly, we denoised the image sequence, and then we used Brox optical flow method to estimate motion of two adjacent frames, based on the results of the displacement field for motion correction. Finally, we extracted vascular perfusion region from the surrounding background based on the differences in gray scale for the magnitude of the rich blood supply area and lack of blood supply area in ultrasound images sequence. The experimental results showed that the algorithm could accurately extract the vascular perfusion area, reach the precision of identification of clinical perfusion area, and only small amount of calculation was needed and the process was fairly simple.
As an important component of the event related potential (ERP), late positive potential (LPP) is an ideal component for studying emotion regulation. This study was focused on processing and analysing the LPP component of the emotional cognitive reappraisal electroencephalogram (EEG) signal. Firstly, we used independent component analysis (ICA) algorithm to remove electrooculogram, electromyogram and some other artifacts based on 16 subjects' EEG data by using EGI 64-channal EEG acquisition system. Secondly, we processed feature extraction of the EEG signal at Pz electrode by using one versus the rest common spatial patterns (OVR-CSP) algorithm. Finally, the extracted LPP component was analysed both in time domain and spatial domain. The results indicated that ① From the perspective of amplitude comparison, the LPP amplitude, which was induced by cognitive reappraisal, was much higher than the amplitude under the condition of watching neural stimuli, but lower than the amplitude under condition of watching negative stimuli; ② from the perspective of time process, the difference between cognitive reappraisal and watching after processing with OVR-CSP algorithm was in the process of range between 0.3 s and 1.5 s; but the difference between cognitive reappraisal and watching after processing with averaging method was during the process between 0.3 s and 1.25 s. The results suggested that OVR-CSP algorithm could not only accurately extract the LPP component with fewer trials compared with averaging method so that it provided a better method for the follow-up study of cognitive reappraisal strategy, but also provide neurophysiological basis for cognitive reappraisal in emotional regulation.
Emotion can reflect the psychological and physiological health of human beings, and the main expression of human emotion is voice and facial expression. How to extract and effectively integrate the two modes of emotion information is one of the main challenges faced by emotion recognition. In this paper, a multi-branch bidirectional multi-scale time perception model is proposed, which can detect the forward and reverse speech Mel-frequency spectrum coefficients in the time dimension. At the same time, the model uses causal convolution to obtain temporal correlation information between different scale features, and assigns attention maps to them according to the information, so as to obtain multi-scale fusion of speech emotion features. Secondly, this paper proposes a two-modal feature dynamic fusion algorithm, which combines the advantages of AlexNet and uses overlapping maximum pooling layers to obtain richer fusion features from different modal feature mosaic matrices. Experimental results show that the accuracy of the multi-branch bidirectional multi-scale time sensing dual-modal emotion recognition model proposed in this paper reaches 97.67% and 90.14% respectively on the two public audio and video emotion data sets, which is superior to other common methods, indicating that the proposed emotion recognition model can effectively capture emotion feature information and improve the accuracy of emotion recognition.
Femtosecond laser small incision lenticule extraction (SMILE) with different residual stromal thicknesses (RST) is set to investigate its effect on corneal biomechanical properties of rabbits in vivo. In this study, 24 healthy adult Japanese rabbits were randomly divided into group A and B. The RST of group A was set 30% of the corneal central thickness (CCT), and the RST of group B was 50% of the CCT. The thickness of the corneal cap in both groups was set one third of CCT. Corneal visualization Scheimpflug technology (Corvis ST) and Pentacam three-dimensional anterior segment analyzer were used to determine corneal biomechanical and morphological parameters before surgery, and 1 week, 1 month and 3 months after surgery. Pearson correlation analysis was used to analyze factors affecting corneal biomechanical parameters after SMILE. The results showed that the corneal stiffness of group A was significantly higher than that of group B at 1 week and 1 month after surgery, and most biomechanical parameters returned to preoperative levels at 3 months postoperatively. The results of correlation analysis showed that postoperative CCT and RST were the main factors affecting corneal biomechanical parameters after SMILE. There was no significant difference in corneal posterior surface height (PE) between 3 months after surgery and before surgery in both two groups. It indicates that although the ability to resist deformation of cornea decreases in SMILE with thicker corneal cap and less RST, there is no tendency to keratoconus, which may be related to the preservation of more anterior stromal layer.
Attention level evaluation refers to the evaluation of people's attention level through observation or experimental testing, and its research results have great application value in education and teaching, intelligent driving, medical health and other fields. With its objective reliability and security, electroencephalogram signals have become one of the most important technical means to analyze and express attention level. At present, there is little review literature that comprehensively summarize the application of electroencephalogram signals in the field of attention evaluation. To this end, this paper first summarizes the research progress on attention evaluation; then the important methods for electroencephalogram attention evaluation are analyzed, including data preprocessing, feature extraction and selection, attention evaluation methods, etc.; finally, the shortcomings of the current development in the field of electroencephalogram attention evaluation are discussed, and the future development trend is prospected, to provide research references for researchers in related fields.
