Aiming at long signal acquisition time, low flux, bad signal-to-noise ratio and low intelligence in coloration biochip reader, a new kind of rapid device with high flux was developed. The device consisted of hardware system and software system. It used a charge-coupled device (CCD) as the photoelectric sensor elements and obtained the biochip microarray image. The device integrated the embedded operating system based on i.MX6 chip. The microarray image processing, data analysis and result output were achieved through the code information of the software chip. Experiments with the standard grayscale sheet and standard format chip were carried out. The results showed that the maximum measurement error was less than 0.1%, the value of R2 was 98.7%, and the value of CV was 1.096 1%. The comparison results of 200 samples showed that detection performance of the proposed device was better than that of the same kind of marketed equipment.
Medical image registration plays an important role in medical diagnosis and treatment planning. However, the current registration methods based on deep learning still face some challenges, such as insufficient ability to extract global information, large number of network model parameters, slow reasoning speed and so on. Therefore, this paper proposed a new model LCU-Net, which used parallel lightweight convolution to improve the ability of global information extraction. The problem of large number of network parameters and slow inference speed was solved by multi-scale fusion. The experimental results showed that the Dice coefficient of LCU-Net reached 0.823, the Hausdorff distance was 1.258, and the number of network parameters was reduced by about one quarter compared with that before multi-scale fusion. The proposed algorithm shows remarkable advantages in medical image registration tasks, and it not only surpasses the existing comparison algorithms in performance, but also has excellent generalization performance and wide application prospects.
A measurement system based on the image processing technology and developed by LabVIEW was designed to quickly obtain the range of motion (ROM) of spine. NI-Vision module was used to pre-process the original images and calculate the angles of marked needles in order to get ROM data. Six human cadaveric thoracic spine segments T7-T10 were selected to carry out 6 kinds of loads, including left/right lateral bending, flexion, extension, cis/counterclockwise torsion. The system was used to measure the ROM of segment T8-T9 under the loads from 1 N·m to 5 N·m. The experimental results showed that the system is able to measure the ROM of the spine accurately and quickly, which provides a simple and reliable tool for spine biomechanics investigators.
We searched and retrieved literature on the topic of medical image processing published on SCI journals in the past 10 years. We then imported the retrieved literature into TDA for data cleanup before data analysis and processing by EXCLE and UCINET to generate tables and figures that could indicate disciplinary correlation and research hotspots from the perspective of bibliometrics. The results indicated that people in Europe and USA were leading researchers on medical image processing with close international cooperation. Many disciplines contributed to the fast development of medical image processing with intense interdisciplinary researches. The papers that we found show recent research hotspots of the algorithm, system, model, image and segmentation in the field of medical image processing. Cluster analysis on key words of high frequency demonstrated complicated clustering relationship.
In order to accurately localize the image coordinates and serial numbers of intraoperative subdural matrix electrodes, a matrix electrode localization algorithm for image processing is proposed in this paper. Firstly, by using point-by-point extended electrode location algorithm, the electrode is expanded point-by-point vertically and horizontally, and the initial coordinates and serial numbers of each electrode are determined. Secondly, the single electrode coordinate region extraction algorithm is used to determine the best coordinates of each electrode, so that the image coordinates and serial numbers of all electrodes are determined point-by-point. The results show that the positioning accuracy of electrode serial number is 100%, and the electrode coordinate positioning error is less than 2 mm. The algorithms in this paper can accurately localize the image coordinates and the serial numbers of a matrix electrode arranged in an arc, which could aid drawing of cortical function mapping, and achieve precise positioning of brain functional areas, so that it can be widely used in neuroscience research and clinical application based on electrocorticogram analysis.
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.
To locate the nuclei in hematoxylin-eosin (HE) stained section images more simply, efficiently and accurately, a new method based on distance estimation is proposed in this paper, which shows a new mind on locating the nuclei from a clump image. Different from the mainstream methods, proposed method avoids the operations of searching the combined singles. It can directly locate the nuclei in a full image. Furthermore, when the distance estimation built on the matrix sequence of distance rough estimating (MSDRE) is combined with the fact that a center of a convex region must have the farthest distance to the boundary, it can fix the positions of nuclei quickly and precisely. In addition, a high accuracy and efficiency are achieved by this method in experiments, with the precision of 95.26% and efficiency of 1.54 second per thousand nuclei, which are better than the mainstream methods in recognizing nucleus clump samples. Proposed method increases the efficiency of nuclear location while maintaining the location's accuracy. This can be helpful for the automatic analysis system of HE images by improving the real-time performance and promoting the application of related researches.
Lung cancer is the most threatening tumor disease to human health. Early detection is crucial to improve the survival rate and recovery rate of lung cancer patients. Existing methods use the two-dimensional multi-view framework to learn lung nodules features and simply integrate multi-view features to achieve the classification of benign and malignant lung nodules. However, these methods suffer from the problems of not capturing the spatial features effectively and ignoring the variability of multi-views. Therefore, this paper proposes a three-dimensional (3D) multi-view convolutional neural network (MVCNN) framework. To further solve the problem of different views in the multi-view model, a 3D multi-view squeeze-and-excitation convolution neural network (MVSECNN) model is constructed by introducing the squeeze-and-excitation (SE) module in the feature fusion stage. Finally, statistical methods are used to analyze model predictions and doctor annotations. In the independent test set, the classification accuracy and sensitivity of the model were 96.04% and 98.59% respectively, which were higher than other state-of-the-art methods. The consistency score between the predictions of the model and the pathological diagnosis results was 0.948, which is significantly higher than that between the doctor annotations and the pathological diagnosis results. The methods presented in this paper can effectively learn the spatial heterogeneity of lung nodules and solve the problem of multi-view differences. At the same time, the classification of benign and malignant lung nodules can be achieved, which is of great significance for assisting doctors in clinical diagnosis.
Objective To lay a foundation for study of optic narve damage in glaucoma by measuring the number and diameter of the optic nerve fibers and optic disc area in normal individuals. Methods The cross-sections of the optic nerve and the optic discs in 15 normal human eyes were examined with the use of a computerized image analysis system. Results The mean nerve fiber count was 10.08times;105plusmn;1.61times;105. The mean nerve fiber diameter was (0.99plusmn;0.04)mu;m. The nerve fiber count increased significantly with the increasing of cross-section area of the optic nerve, but the nerve fiber count was independent of the optic dise area. Conclusion This study provided anatomic basis for predicting the prognosis of optic nerve damage and further studyv of nerve damage in glaucoma. (Chin J Ocul Fundus Dis,1999,15:16-19)