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.
Purpose To investigate the pattern of subretinal neovascular membrane(SRNVM)in central exudative chorioretinitis(CEC). Methods With the help of a PC microcomputer,we performed a quantitative measurement of SRNVM in 32 eyes of 32 patients with Rieger is CEC. Results SRNVM-optic disc area ratio were 0.1151plusmn;0.0842．The foveola was on the top of SRNVM in 7 cases．The other 25 of SRNVMs were scattered in macular area around foveola,and 2 of them were nasal to it．The distance between the edge of SRNVM and foveola was less than 175mu;m in 13 cases,175～300mu;m in 4 cases and more than 300mu;m in 15 cases． Conclusion To be compared with the previous data,the present results suggested that laser photocoagulation might be one of the most important therapies for SRNVM in Rieger is CEC． (Chin J Ocul Fundus Dis,1998,14:114-115)
Objective To determine feasibility of texture analysis of non-enhanced CT scan for differential diagnosis of liver cancer and hepatic hemangioma. Methods Fifty-six patients with liver cancer or hepatic hemangioma confirmed by pathology were enrolled in this retrospective study. After exclusion of images of 4 patients with artifacts and lesion diameter less than 1.0 cm, images of 52 patients (57 lesions) were available to further analyze. Texture features derived from the gray-level histogram, co-occurrence and run-length matrix, absolute gradient, autoregressive model, and wavelet transform were calculated. Fisher, probability of classification error and average correlation (POE+ACC), and mutual information coefficients (MI) were used to extract 10 optimized texture features. The texture characteristics were analyzed by using linear discriminant analysis (LDA) and nonlinear discriminant analysis (NDA) provided by B11 module in the Mazda software, the minimum error probability of differential diagnosis of liver cancer and hepatic hemangioma was calculated. Most discriminating features (MDF) of LDA was applied to K nearest neighbor classification (KNN); NDA to extract the data used in artificial neural network (ANN) for differential diagnosis. Results The NDA/ANN-POE+ACC was the best for identifying liver cancer and hepatic hemangioma, and the minimum error probability was the lowest as compared with the LDA/KNN-Fisher, LDA/KNN-POE+ACC, LDA/KNN-MI, NDA/ANN-Fisher, and NDA/ANN-MI respectively, the differences were statistically significant (χ2=4.56, 4.26, 3.14, 3.14, 3.33;P=0.020, 0.018, 0.026, 0.026, 0.022). Conclusions The minimum error probability is low for different texture feature selection methods and different analysis methods of Mazda texture analysis software in identifying liver cancer and hepatic hemangioma, and NDA/ANN-POE+ACC method is best. So it is feasible to use texture analysis of non-enhanced CT images to identify liver cancer and hepatic hemangioma.
Objective To achieve threedimensional (3D) contour image of boneand articular cartilage for fabricating custommade artificial semiknee joint as segment bone allograft.Methods The distal femora of human and pig were scanned with Picker 6000 spiral X-ray computed tomography with 1.0 mm thick slice. The data obtained were treated in Voxel Q image workstation for 3D reconstruction with volume rendering technique. After being downloaded to personal computer at 0.1 mm interval, the transaxial 2D image data were converted into 2D digitized contour data by using image processing software developed by the team. The 2D digitized data were inputted into image processing software of Surfacer 9.0 (Imageware Company, USA), then the 3D wire frame and solidimages of femoral condyle were reconstructed. Subsequently， based on the clinical experience and the requirement of the design of artificial knee joint, the 3Dcontour image of bone or articular cartilage was extracted from the surrounding.Results The 3D contour image of bone or articular cartilage presented was edited and processed easily for the computer aided design(CAD) of custom-madeartificial knee joint.Conclusion The 3D contour image of boneand articular cartilage can be obtained by spiral CT scanning, and the digitized data can beapplied directly to CAD of custom-made artificial joint and subsequently rapidprototyping fabricating. In addition, the reconstruction method is simple and can be applied widely to clinical implant fabricating practice of dentistry and orthopaedics.
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.
To solve the problems of noise interference and edge signal weakness for the existing medical image, we used two-dimensional wavelet transform to process medical images. Combined the directivity of the image edges and the correlation of the wavelet coefficients, we proposed a medical image processing algorithm based on wavelet characteristics and edge blur detection. This algorithm improved noise reduction capabilities and the edge effect due to wavelet transformation and edge blur detection. The experimental results showed that directional correlation improved edge based on wavelet transform fuzzy algorithm could effectively reduce the noise signal in the medical image and save the image edge signal. It has the advantage of the high-definition and de-noising ability.
Objective To improve the fitness and initial fixation strength between the hip and bone and to optimize the shape of the prosthetic implants. Methods The cross-section of hip canal was automatically extracted by Image processing. By using taper curve fit,hypocurve predigesting and the curve of shape center fit, the parameters of long-short diameter and the curve of shape center were got to design the hip shape.CAD was adopted to analyze and evaluate the configuration of bone and shape of hip.The “peg-in-hole” was employed to optimize the shape of implant by the visual test of “Drawingout” in computer. Results 23.2% hip-bone average matching rate and 0.033% bone damage rate were presented by CAD analysis. The implant extraction path were validated visually and quantitatively by measuring the maximum amount of overlap in the path configuration. Conclusion The valuable method for prothsetic hip design was presented by the way of image processing,graphics design and optimizingdesign in this study.
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.