Automated characterization of different vessel wall tissues including atherosclerotic plaques, branchings and stents from intravascular ultrasound (IVUS) gray-scale images was addressed. The texture features of each frame were firstly detected with local binary pattern (LBP), Haar-like and Gabor filter in the present study. Then, a Gentle Adaboost classifier was designed to classify tissue features. The methods were validated with clinically acquired image data. The manual characterization results obtained by experienced physicians were adopted as the golden standard to evaluate the accuracy. Results indicated that the recognition accuracy of lipidic plaques reached 94.54%, while classification precision of fibrous and calcified plaques reached 93.08%. High recognition accuracy can be reached up to branchings 93.20% and stents 93.50%, respectively.
Skin aging is the most intuitive and obvious sign of the human aging processes. Qualitative and quantitative determination of skin aging is of particular importance for the evaluation of human aging and anti-aging treatment effects. To solve the problem of subjectivity of conventional skin aging grading methods, the self-organizing map (SOM) network was used to explore an automatic method for skin aging grading. First, the ventral forearm skin images were obtained by a portable digital microscope and two texture parameters, i.e., mean width of skin furrows and the number of intersections were extracted by image processing algorithm. Then, the values of texture parameters were taken as inputs of SOM network to train the network. The experimental results showed that the network achieved an overall accuracy of 80.8%, compared with the aging grading results by human graders. The designed method appeared to be rapid and objective, which can be used for quantitative analysis of skin images, and automatic assessment of skin aging grading.
The chemical extraction method was used to prepare the rat uterine decellularized scaffolds, and to investigate the feasibility of preparing the extracellular matrix (ECM) hydrogel. The rat uterus were collected and extracted by 1%sodium dodecyl sulfate (SDS), 3% TritonX-100 and 4% sodium deoxycholate (SDC) in sequence. Scanning electron microscopy, histochemical staining and immunohistochemistry was used to assess the degree of decellularization of rat uterine scaffold. The prepared decellularized scaffold was digested with pepsin to obtain a uterine ECM hydrogel, and the protein content of ECM was determined by specific ELISA kit. Meanwhile, the mechanical characteristic of ECM hydrogel was measured. The results showed that the chemical extraction method can effectively remove the cells effectively in the rat uterine decellularized scaffold, with the ECM composition preserved completely. ECM hydrogel contains a large amount of ECM protein and shows a good stability, which provides a suitable supporting material for the reconstruction of endometrium in vitro.
The brain computer interface (BCI) can be used to control external devices directly through electroencephalogram (EEG) information. A multi-linear principal component analysis (MPCA) framework was used for the limitations of tensor form of multichannel EEG signals processing based on traditional principal component analysis (PCA) and two-dimensional principal component analysis (2DPCA). Based on MPCA, we used the projection of tensor-matrix to achieve the goal of dimensionality reduction and features exaction. Then we used the Fisher linear classifier to classify the features. Furthermore, we used this novel method on the BCI competitionⅡdataset 4 and BCI competitionⅣdataset 3 in the experiment. The second-order tensor representation of time-space EEG data and the third-order tensor representation of time-space-frequency EEG data were used. The best results that were superior to those from other dimensionality reduction methods were obtained by much debugging on parameter P and testQ. For two-order tensor, the highest accuracy rates could be achieved as 81.0% and 40.1%, and for three-order tensor, the highest accuracy rates were 76.0% and 43.5%, respectively.
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.
Medical image fusion realizes advantage integration of functional images and anatomical images. This article discusses the research progress of multi-model medical image fusion at feature level. We firstly describe the principle of medical image fusion at feature level. Then we analyze and summarize fuzzy sets, rough sets, D-S evidence theory, artificial neural network, principal component analysis and other fusion methods' applications in medical image fusion and get summery. Lastly, we in this article indicate present problems and the research direction of multi-model medical images in the future.
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.
Gait recognition is a new technology in biometric recognition and medical treatment which has advantages such as long-distance and non-invasiveness. Depending on the differences between different people's walking postures, we can recognize individuals by characteristics extracted from the images of walking movement. A complete gait recognition process usually includes gait sequence acquisition, gait detection, feature extracting and recognition. In this paper, the commonly used methods of these four processes are introduced, and feature extraction methods are classified from different multi-angle views. And then the new algorithm of multi-view emerged in recent years is highlighted. In addition, this paper summarizes the existing difficulties of gait recognition, and looks into the future development trends of it.
Sleep apnea causes cardiac arrest, sleep rhythm disorders, nocturnal hypoxia and abnormal blood pressure fluctuations in patients, which eventually lead to nocturnal target organ damage in hypertensive patients. The incidence of obstructive sleep apnea hypopnea syndrome (OSAHS) is extremely high, which seriously affects the physical and mental health of patients. This study attempts to extract features associated with OSAHS from 24-hour ambulatory blood pressure data and identify OSAHS by machine learning models for the differential diagnosis of this disease. The study data were obtained from ambulatory blood pressure examination data of 339 patients collected in outpatient clinics of the Chinese PLA General Hospital from December 2018 to December 2019, including 115 patients with OSAHS diagnosed by polysomnography (PSG) and 224 patients with non-OSAHS. Based on the characteristics of clinical changes of blood pressure in OSAHS patients, feature extraction rules were defined and algorithms were developed to extract features, while logistic regression and lightGBM models were then used to classify and predict the disease. The results showed that the identification accuracy of the lightGBM model trained in this study was 80.0%, precision was 82.9%, recall was 72.5%, and the area under the working characteristic curve (AUC) of the subjects was 0.906. The defined ambulatory blood pressure features could be effectively used for identifying OSAHS. This study provides a new idea and method for OSAHS screening.
Early screening based on computed tomography (CT) pulmonary nodule detection is an important means to reduce lung cancer mortality, and in recent years three dimensional convolutional neural network (3D CNN) has achieved success and continuous development in the field of lung nodule detection. We proposed a pulmonary nodule detection algorithm by using 3D CNN based on a multi-scale attention mechanism. Aiming at the characteristics of different sizes and shapes of lung nodules, we designed a multi-scale feature extraction module to extract the corresponding features of different scales. Through the attention module, the correlation information between the features was mined from both spatial and channel perspectives to strengthen the features. The extracted features entered into a pyramid-similar fusion mechanism, so that the features would contain both deep semantic information and shallow location information, which is more conducive to target positioning and bounding box regression. On representative LUNA16 datasets, compared with other advanced methods, this method significantly improved the detection sensitivity, which can provide theoretical reference for clinical medicine.