ABSTRACT
Objective::To construct the color matching template irrelevant to size and rotation according to the types and distribution characteristics of colors in images of Chinese herbal pieces, in order to establish color characterization and image identification methods for Chinese herbal pieces. Method::Totally 20 types of Chinese herbal pieces were selected, including rhizomes, flowers, seeds and fruits.For each sample, two observation surfaces were selected to extract color parameters in foreground through image processing such as image segmentation, model transformation from RGB to L*a*b*.Color vectors of the two observation surfaces were sequenced in a descending order, scaled to a certain size by interpolating, and combined into an integrated color vector in a weight ratio of 1∶1.As for centripetally distributed observation surface images(e.g.transverse section), corrosion operation was conducted to extract the color components of each ring from outer to inner by circles, which were then ordered and scaled.The integrated color vector was used as initial template for training, the correlation coefficient between each sample and the template was calculated, and the interval estimation of positive samples were carried out by t test.With the total recognition rate as an indicator, the optimal template dimensions, width of ring and training volume were ultimately determined. Result::The visualization results of the trained templates of the varied herbal pieces were easy to be visually distinguished.After 260 samples of the herbal pieces were tested, the template of a and b components was better than that of L*, a* and b* in terms of recognition performance, with a* recognition accuracy of 95.8%(249/200). Conclusion::Color characteristics of images from two observation surfaces of Chinese herbal pieces are integrated to obtain the combined color feature vector, so as to achieve preferable recognition results for samples from both the same and different medicinal parts.This method boasts a strong anti-interference ability of random variation of sample shape, sampling part and color.
ABSTRACT
Objective:To build a gray-level matching template by using the gray level information of the microscopic image of the transverse section of Chinese medicinal materials,in order to realize the automatic recognition of the images of Chinese medicinal materials independent of scale and orientation. Method:By using the embedding method of polyethylene glycol (PEG),the transverse slices of 19 kinds of common rhizomatous medicinal materials were obtained. The images of the slices were taken by digital microscopic imaging technology,and the mosaic grayscale images were obtained by image registration,noise removal and boundary location. The center of the structure of the materials in the images was selected to establish the polar coordinate system, so as to divide grids from the radial and angular directions. By counting the gray information in each grid,the gray information digital matrix that can characterize the microscopic identification characteristics of the materials was obtained. Images in an appropriate sample size was used to train the matrix for generalization of the matrix. The covariance coefficients between the matrix of positive or negative verification sample and the template matrix were calculated to set the best identification parameters. For each medicinal material,80 fan-shaped images were prepared,including 70% of training samples,15% of validation samples and 15% of test samples,and single template and template set were tested with test samples. Result:In the test of 240 images including non-template-set medicinal materials,the correct recognition rate of single-template test was 90.1%,and that of template-set test was 92.5%. Conclusion:This method can well characterize the microscopic identification characteristics of Chinese medicinal materials, with a strong anti-interference ability and less subjective-errors, acquire sample images easily, and provide technical support for the digitization of morphological quality control of Chinese medicinal materials.
ABSTRACT
Despite Apolipoprotein E (ApoE) being one of the main apolipoproteins in the blood, the association between its genotype and the high cholesterol or blood glucose levels commonly seen in clinical practice is inconclusive. Such research is also lacking in the Han population. The aim of this study was to investigate the association between APOE genotype, diabetes, and plasma glucose and lipid levels. We included 243 community-dwelling elderly residents in this study. Participant APOE genotypes were assessed and were simultaneously tested for weight, height, blood glucose, triglycerides, cholesterol, and high- and low-density lipoprotein. In addition, gender, age, years of education, cognitive function, and medical history was recorded. Subjects were divided into 3 groups based on APOE genotype: APOE ε2 group (ε2/ε2 and ε2/ε3), APOE ε3 group (ε3/ε3), and APOE ε4 group (ε2/ε4, ε3/ε4 and ε4/ε4). Comparisons between groups were conducted for the incidence of diabetes, high blood pressure, and dementia, as well as for differences in body-mass index, fasting plasma glucose, and blood lipids. The APOE ε3/ε3 genotype exhibited the highest frequency (70.4%) among the subjects. Participants in the APOE ε3 group demonstrated significantly higher levels of fasting plasma glucose than those in the APOE ε2 and APOE ε4 groups (P<0.05). The APOE ε3 group had slightly higher abnormal fasting plasma glucose values than did the APOE ε2 group (P = 0.065). Furthermore, the APOE3 genotype was significantly correlated with both fasting plasma glucose level and glucose abnormality (P< 0.05) and trended toward statistically significant correlation with diabetes (P = 0.082). The correlation between APOE2 and low low-density lipoprotein levels also approached statistical significance (P = 0.052). Thus, elderly community dwelling residents of Han ethnicity carrying the APOE ε3/ε3 genotype might have higher plasma glucose levels and a higher occurrence of diabetes.
