In order to solve the saturation distortion phenomenon of R component in fingertip video image, this paper proposes an iterative threshold segmentation algorithm, which adaptively generates the region to be detected for the R component, and extracts the human pulse signal by calculating the gray mean value of the region to be detected. The original pulse signal has baseline drift and high frequency noise. Combining with the characteristics of pulse signal, a zero phase digital filter is designed to filter out noise interference. Fingertip video images are collected on different smartphones, and the region to be detected is extracted by the algorithm proposed in this paper. Considering that the fingertip’s pressure will be different during each measurement, this paper makes a comparative analysis of pulse signals extracted under different pressures. In order to verify the accuracy of the algorithm proposed in this paper in heart rate detection, a comparative experiment of heart rate detection was conducted. The results show that the algorithm proposed in this paper can accurately extract human heart rate information and has certain portability, which provides certain theoretical help for further development of physiological monitoring application on smartphone platform.
ObjectiveTo study the preservation effect of true bone ceramics (TBC) prepared by high-temperature calcination of bovine bone on alveolar ridge of canine extraction socket.MethodsSix healthy Beagle dogs (aged 1.5-2 years) were selected to extract the second and fourth premolars of both mandibles and the second premolars of the maxilla. The left extraction socket was implanted with TBC as the experimental group, and the right side was implanted with the calcined bovine bone (CBB) as the control group, to observe the alveolar ridge preservation effect. Three dogs were euthanized after general observation at 1 and 6 months after operation respectively. After separating the maxilla and mandible, cone beam CT (CBCT) was performed to measure the average gray value of the graft site and the adjacent reference area (the area between the roots of the adjacent third premolar) and calculate the gray scale ratio between the bone graft site and the reference area. Histological observation was made on the bone graft site to evaluate the new bone formation.ResultsGeneral observation showed that the wounds of both groups were basically healed at 2 weeks after operation, and the bone graft materials were not exposed. The wounds healed well at 1 and 6 months after operation without swelling. The results of CBCT showed that the residual material was found in both groups at 1 month after operation, and no significant residual material was found in both groups at 6 months after operation, and the alveolar ridge height of the bone graft area was not significantly reduced. There was no significant difference in the bone mineral density between the experimental group and the control group. The gray scale ratios of the experimental group at 1 month and 6 months after operation were 0.97±0.14 and 0.93±0.06, respectively, and were 0.99±0.16 and 0.94±0.05 in control group, showing no significant difference between the two groups (t=?1.030, P=0.333; t=?0.770, P=0.466). HE staining observation showed that a large number of bone graft materials did not degrade and new bone formed around the grafts in both groups at 1 month after operation; the bone graft materials were absorbed and a large number of new bones were formed in both groups at 6 months after operation.ConclusionTBC can maintain bone mineral density and have good osteoconductivity in the alveolar ridge site preservation experiment of dogs, and can be used for alveolar ridge site preservation.
Objective To observe the histomorphology and the biocompatibil ity of acellular nerve prepared by different methods, to provide the experimental evidence for the selection of preparation of acellular nerve scaffold. Methods Forty-eight adult Sprague Dawley rats, male or female, weighing 180-220 g, were selected. The sciatic nerves were obtained from 30 rats and were divided into groups A, B, and C (each group had 20 nerves). The acellular sciatic nerves were prepared by the chemical methods of Dumont (group A), Sondell (group B), and Haase (group C). The effect to remove cells was estimated by the degree of decellularization, degree of demyel ination, and intergrity of nerve fiber tube. The histocompatibil ity was observed by subcutaneous implant test in another 18 rats. Three points were selected along both sides of centre l ine on the back of rats, and the points were randomly divided into groups A1, B1, and C1; the acellular nerve of groups A, B, and C were implanted in the corresponding groups A1, B1, and C1. At 1, 2, and 4 weeks after operation, the rats were sacrificed to perform the general observation and histological observation. Results The histomorphology: apart of cells and the dissolved scraps of axon could be seen in acellular never in the group A, and part of Schwann cell basilar membrane was broken. In group B, the cells in the acellular never were not removed completely, the Schwann cell basilar membrane formed bigger irregular hollows, part of the Schwann cell basilar membrane was broken obviously. But in the group C, the cells were completely removed, the Schwann cell basilar membrane remained intactly. Group C was better than group A and group B in the degree of decellularization, degree of demyel ination, integrity of nerve fiber tube and total score, showing significant differences (P lt; 0.05). The subcutaneous implant test: there were neutrophils and lymphocytes around the acellular nerve in 3 groups at 1 week after implant. A few of lymphocytes were observed around the acellular nerve in 3 groups at 2 weeks after implant. The inflammation was less in groups A1, B1, and C1 at 4 weeks after implant, part of the cells grew into the acellular nerve and arranged along the Schwann cell basilar membrane. The reaction indexes of the inflammational cells in group A1 and group B1 were higher than that in group C1 at 1, 2, and 4 weeks after implant, showing significant differences (P lt; 0.01), but there was no significant difference between group A1 and group B1 (P gt; 0.05). Conclusion The acellular sciatic nerves prepared by Haase method has better acellular effect and the histocompatibil ity than those by the methods of Dumont and Sondell.
Purpose To explore the characteristics of eyes after congenital cataract surgery and to evaluate the methods of different retinal detachment surgery in those eyes． Method We retrospeetively reviewed the cli ncal data of 44 eyes with rhegmatogenous retinal detachment (RRD) after congenital cataract surgery,and compared the surgical results between scleral buckling and vitrectomy in those eyes．The mean interval between the congenital cataract surgery and RRD of the affectde eyes was 14．8 years and most of the techniques of cataract surgery was irrigation-aspiration and capsulotomy was performed in nearly all eyes． The mean axis length in 16 eyes was （26.8plusmn;1.90） mm． Results The success rate was 80．3% in scleral buckling and 85．7% in vitrectomy． Conclusion There is a long interval between congenital cataract surgery and RD．The pupil of these eyes is often small and immobile,causing diffculty in visualizing the peripheral retina ,decreasing the success rate of scleral buckling operation．Vitrectomy is an ideal chocie for such eyes． (Chin J Ocul Fundus Dis，2000，16：71-138)
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)
Existing arrhythmia classification methods usually use manual selection of electrocardiogram (ECG) signal features, so that the feature selection is subjective, and the feature extraction is complex, leaving the classification accuracy usually affected. Based on this situation, a new method of arrhythmia automatic classification based on discriminative deep belief networks (DDBNs) is proposed. The morphological features of heart beat signals are automatically extracted from the constructed generative restricted Boltzmann machine (GRBM), then the discriminative restricted Boltzmann machine (DRBM) with feature learning and classification ability is introduced, and arrhythmia classification is performed according to the extracted morphological features and RR interval features. In order to further improve the classification performance of DDBNs, DDBNs are converted to deep neural network (DNN) using the Softmax regression layer for supervised classification in this paper, and the network is fine-tuned by backpropagation. Finally, the Massachusetts Institute of Technology and Beth Israel Hospital Arrhythmia Database (MIT-BIH AR) is used for experimental verification. For training sets and test sets with consistent data sources, the overall classification accuracy of the method is up to 99.84% ± 0.04%. For training sets and test sets with inconsistent data sources, a small number of training sets are extended by the active learning (AL) method, and the overall classification accuracy of the method is up to 99.31% ± 0.23%. The experimental results show the effectiveness of the method in arrhythmia automatic feature extraction and classification. It provides a new solution for the automatic extraction of ECG signal features and classification for deep learning.
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.
Optical coherence tomography (OCT) is a new technique applied in cardiovascular system. It can detect vessel intimal, small structure of plaque surface and discover small lesions with its high axial resolution and quantification character. Especially with the application of OCT in characterization of coronary atherosclerotic plaque, diagnosis and treatment strategy making, optimizing percutaneous coronary intervention therapy and assessment after stent planting make the OCT become an efficient tool for cardiovascular disease diagnosis and treatment. This paper presents a novel coronary vessel intimal sequence extraction method based on prior boundary constraints in OCT image. On the basis of conventional Chan-Vese model, we modified the evolutionary weight function to control the evolutionary rate of boundary by adding local information of boundary curve. At the same time, we added the gradient energy term and intimal boundary constraint term based on priori boundary condition to further control the evolutionary of boundary curve. At last, coronary vessel intimal is extracted in a sequence way. The comparison with vessel intimal, manual segmented by clinical scientists (golden standard), indicates that our coronary vessel intimal extraction method is robust to intimal boundary blur, distortion, guide wire shadow and plaque disturbs. The results of this study can be applied to clinical aid diagnosis and precise diagnosis and treatment.
In order to solve the problem of early classification of Alzheimer’s disease (AD), the conventional linear feature extraction algorithm is difficult to extract the most discriminative information from the high-dimensional features to effectively classify unlabeled samples. Therefore, in order to reduce the redundant features and improve the recognition accuracy, this paper used the supervised locally linear embedding (SLLE) algorithm to transform multivariate data of regional brain volume and cortical thickness to a locally linear space with fewer dimensions. The 412 individuals were collected from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) including stable mild cognitive impairment (sMCI, n = 93), amnestic mild cognitive impairment (aMCI, n = 96), AD (n = 86) and cognitive normal controls (CN, n = 137). The SLLE algorithm used in this paper is to calculate the nearest neighbors of each sample point by adding the distance correction term, and the locally linear reconstruction weight matrix was obtained from its nearest neighbors, then the low dimensional mapping of the high dimensional data can be calculated. In order to verify the validity of SLLE in the task of classification, the feature extraction algorithms such as principal component analysis (PCA), Neighborhood MinMax Projection (NMMP), locally linear mapping (LLE) and SLLE were respectively combined with support vector machines (SVM) classifier to obtain the accuracy of classification of CN and sMCI, CN and aMCI, CN and AD, sMCI and aMCI, sMCI and AD, and aMCI and AD, respectively. Experimental results showed that our method had improvements (accuracy/sensitivity/specificity: 65.16%/63.33%/67.62%) on the classification of sMCI and aMCI by comparing with the combination algorithm of LLE and SVM (accuracy/sensitivity/specificity: 64.08%/66.14%/62.77%) and SVM (accuracy/sensitivity/specificity: 57.25%/56.28%/58.08%). In detail the accuracy of the combination algorithm of SLLE and SVM is 1.08% higher than the combination algorithm of LLE and SVM, and 7.91% higher than SVM. Thus, the combination of SLLE and SVM is more effective in the early diagnosis of Alzheimer’s disease.