Skin aging principles play important roles in skin disease diagnosis, the evaluation of skin cosmetic effect, forensic identification and age identification in sports competition, etc. This paper proposes a new method to evaluate the skin aging objectively and quantitatively by skin texture area. Firstly, the enlarged skin image was acquired. Then, the skin texture image was segmented by using the iterative threshold method, and the skin ridge image was extracted according to the watershed algorithm. Finally, the skin ridge areas of the skin texture were extracted. The experiment data showed that the average areas of skin ridges, of both men and women, had a good correlation with age (the correlation coefficient r of male was 0.938, and the correlation coefficient r of female was 0.922), and skin texture area and age regression curve showed that the skin texture area increased with age. Therefore, it is effective to evaluate skin aging objectively by the new method presented in this paper.
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.
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.
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.
Based on repeated experiments as well as continuous researching and improving, an efficient scheme to measure velocity and displacement of the coxa and knee movements based on video image processing technique is presented in this paper. The scheme performed precise and real-time quantitative measurements of 2D velocity or displacement of the coxa and knee using a video camera mounted on one side of the healing and training beds. The beds were based on simplified pinhole projection model. In addition, we used a special-designed auxiliary calibration target, composed by 24 circle points uniformly located on two concentric circles and two straight rods which can rotate freely along the concentric center within the vertical plane, to do the measurements. Experiments carried out in our laboratory showed that the proposed scheme could basically satisfy the requirements about precision and processing speed of such kind of system, and would be very suitable to be applied to smart evaluation/training and healing system for muscles/balance function disability as an advanced and intuitional helping method.
In the study of embryo development process, the morphological features at different stages are essential to evaluate developmental competence of the embryo, which can be used to optimize and improve the system for in-vitro embryo culture. In this paper, an online monitoring system was designed for long-term culture of embryos, based on a monitoring strategy of low-magnification search and high-magnification observation. Three optical modules of 4× phase contrast, 10× and 20× Hoffman modulation phase contrast were configured in this system to meet the requirements of different fields of view, especially when the size of the embryo increases during the culture. Using an optomechanical system matching design, an error control and alignment test, the resolution of optical imaging was guaranteed, and a relief stereoscopic imaging with high contrast of embryos was obtained. Through low-magnification field of view to identify and locate embryos and high-magnification field of view to capture the details, the system realized online tracking and monitoring of embryos. In addition, we developed and verified an embryo identifying and locating algorithm based on image contour area and definition evaluation. The online monitoring system of in-vitro embryo culture proposed in this paper can track and record the morphological features of embryos without affecting the embryo development, providing a basis for the assessment of embryo development and the optimization of in-vitro culture system.
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.
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.
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.
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.