Jiatuo Xu

A medical image color correction method base on supervised color constancy

Objective: This paper aims to establish an acquisition and analysis method for medical image in the indoor natural light conditions; Method: According to the color characteristics of the medical image and the constancy of supervised color, we make the color codes which are used for a* b* value correction, and gray codes which are used for L* value correction. Then, we carry on topological subdivision to the one-dimensional L* space and the two-dimensional a*b* space respectively. We establish the mapping function ldquodecomposition-projection-restorationrdquo, and ran the linear mapping to the one-dimensional L* space and triangle mapping to the two-dimensional a*b* space. We correct the 198 color blocks in 22 pictures. Result: Compared with the standard value, DeltaL*, DeltaC*, DeltaE decreased significantly (P<0.01), imagepsilas color difference is reduced, color saturation and the value is closer to standard value. Conclusion: TRM(topology resolve-map model) method can significantly adjust the color error of tongue images under the indoor natural light condition and easy to use, and has a good performance of color correction. It is feasible to apply this method to the color correction of the medical images.

A Diagnostic Method Based on Tongue Imaging Morphology

The tongue morphology analysis is an important part of imaging diagnosis of tongue inspection. This paper studies and analyzes the tongue shape to establish a kind of tongue diagnosis method based on tongue images. CCD devices were employed to acquire the front and lateral images of tongue, and then measure the tongues length (L), width (K) and height (H). Furthermore, in 500 testees with possible disorders, the coefficient of variation(CV) was corrected and adjusted to establish an optimum formula between the body surface area (Mt) and the sum of the width of the tongue (K) and height (H). Clinical studies show that this tongue judgment method is of high accuracy. The accuracy rate for fat tongue and thin tongue reaches 93.40% and 88.57% respectively. In addition, this method has been proven to be valuable in the diagnosis of diabetes mellitus, hypertension, chronic gastritis and hyperthyroidism.

Diagnostic Method of Diabetes Based on Support Vector Machine and Tongue Images

Objective. The purpose of this research is to develop a diagnostic method of diabetes based on standardized tongue image using support vector machine (SVM). Methods. Tongue images of 296 diabetic subjects and 531 nondiabetic subjects were collected by the TDA-1 digital tongue instrument. Tongue body and tongue coating were separated by the division-merging method and chrominance-threshold method. With extracted color and texture features of the tongue image as input variables, the diagnostic model of diabetes with SVM was trained. After optimizing the combination of SVM kernel parameters and input variables, the influences of the combinations on the model were analyzed. Results. After normalizing parameters of tongue images, the accuracy rate of diabetes predication was increased from 77.83% to 78.77%. The accuracy rate and area under curve (AUC) were not reduced after reducing the dimensions of tongue features with principal component analysis (PCA), while substantially saving the training time. During the training for selecting SVM parameters by genetic algorithm (GA), the accuracy rate of cross-validation was grown from 72% or so to 83.06%. Finally, we compare with several state-of-the-art algorithms, and experimental results show that our algorithm has the best predictive accuracy. Conclusions. The diagnostic method of diabetes on the basis of tongue images in Traditional Chinese Medicine (TCM) is of great value, indicating the feasibility of digitalized tongue diagnosis.

Syndrome Differentiation Analysis on Mars500 Data of Traditional Chinese Medicine

Mars500 study was a psychological and physiological isolation experiment conducted by Russia, the European Space Agency, and China, in preparation for an unspecified future manned spaceflight to the planet Mars. Its intention was to yield valuable psychological and medical data on the effects of the planned long-term deep space mission. In this paper, we present data mining methods to mine medical data collected from the crew consisting of six spaceman volunteers. The synthesis of the four diagnostic methods of TCM, inspection, listening, inquiry, and palpation, is used in our syndrome differentiation. We adopt statistics method to describe the syndrome factor regular pattern of spaceman volunteers. Hybrid optimization based multilabel (HOML) is used as feature selection method and multilabel k-nearest neighbors (ML-KNN) is applied. According to the syndrome factor statistical result, we find that qi deficiency is a base syndrome pattern throughout the entire experiment process and, at the same time, there are different associated syndromes such as liver depression, spleen deficiency, dampness stagnancy, and yin deficiency, due to differences of individual situation. With feature selection, we screen out ten key factors which are essential to syndrome differentiation in TCM. The average precision of multilabel classification model reaches 80%.

Towards the objectification of tongue diagnosis: the degree of tooth-marked

Tongue diagnosis is one of the most valuable and widely used diagnostic methods in traditional Chinese medicine (TCM). A tooth-marked tongue is an important objective index of the diagnosis of qi (yang) deficiency syndrome during the diagnosis of the TCM. In order to accurately analyze it, an effective method to confirm the amount of tooth-marked and to attain the degree of tooth-marked is brought forward. It sufficiently applies the characters of GVF snake and the features of the curvatures and gradients in the all points of tongue contour. The experiment results demonstrate the efficiency of our proposed algorithm.

Tongue Images Classification Based on Constrained High Dispersal Network

Computer aided tongue diagnosis has a great potential to play important roles in traditional Chinese medicine (TCM). However, the majority of the existing tongue image analyses and classification methods are based on the low-level features, which may not provide a holistic view of the tongue. Inspired by deep convolutional neural network (CNN), we propose a novel feature extraction framework called constrained high dispersal neural networks (CHDNet) to extract unbiased features and reduce human labor for tongue diagnosis in TCM. Previous CNN models have mostly focused on learning convolutional filters and adapting weights between them, but these models have two major issues: redundancy and insufficient capability in handling unbalanced sample distribution. We introduce high dispersal and local response normalization operation to address the issue of redundancy. We also add multiscale feature analysis to avoid the problem of sensitivity to deformation. Our proposed CHDNet learns high-level features and provides more classification information during training time, which may result in higher accuracy when predicting testing samples. We tested the proposed method on a set of 267 gastritis patients and a control group of 48 healthy volunteers. Test results show that CHDNet is a promising method in tongue image classification for the TCM study.

An association analysis between psychophysical characteristics and genome-wide gene expression changes in human adaptation to the extreme climate at the Antarctic Dome Argus

Genome-wide gene expression measurements have enabled comprehensive studies that integrate the changes of gene expression and phenotypic information to uncover their novel associations. Here we reported the association analysis between psychophysical phenotypes and genome-wide gene expression changes in human adaptation to one of the most extreme climates on Earth, the Antarctic Dome Argus. Dome A is the highest ice feature in Antarctica, and may be the coldest, driest and windiest location on earth. It is considered unapproachable due to its hostile environment. In 2007, a Chinese team of 17 male explorers made the expedition to Dome A for scientific investigation. Overall, 133 psychophysical phenotypes were recorded, and genome-wide gene expression profiles from the blood samples of the explorers were measured before their departure and upon their arrival at Dome A. We found that mood disturbances, including tension (anxiety), depression, anger and fatigue, had a strong, positive, linear relationship with the level of a male sex hormone, testosterone, using the Pearson correlation coefficient (PCC) analysis. We also demonstrated that significantly lowest-level Gene Ontology groups in changes of gene expression in blood cells with erythrocyte removal were consistent with the adaptation of the psychophysical characteristics. Interestingly, we discovered a list of genes that were strongly related to significant phenotypes using phenotype and gene expression PCC analysis. Importantly, among the 70 genes that were identified, most were significantly related to mood disturbances, where 42 genes have been reported in the literature mining, suggesting that the other 28 genes were likely novel genes involved in the mood disturbance mechanism. Taken together, our association analysis provides a reliable method to uncover novel genes and mechanisms related to phenotypes, although further studies are needed.

Steady-state transport characteristics of photoconductor based on dye-sensitized solar cell

In the present work, three photoconductors based on dye-sensitized nanocrystalline TiO2 are designed with two dye-sensitized solar cells (DSSCs) connected together using a common counter electrode but different connecting approaches for electrolytes and TiO2 film. DC steady-state transport measurements on source and drain corresponding to the photoanodes of two constituent DSSC units, respectively, show that the three photoconductive devices exhibit similar transistor characteristics, regardless of their different electronic connecting approaches. It is revealed that their transport characteristics are determined by the effective areas of the photoanode and the counter electrode, rather than the connection of electrolytes and TiO2 film. Furthermore, it is demonstrated that the dominant factor of transport behavior is the imbalanced energy band caused by the match of intrinsic potential within two constituent DSSC units. Due to unique mechanism and relatively simple fabrication process, the present phototr...

The Classification of Tongue Colors with Standardized Acquisition and ICC Profile Correction in Traditional Chinese Medicine

Background and Goal. The application of digital image processing techniques and machine learning methods in tongue image classification in Traditional Chinese Medicine (TCM) has been widely studied nowadays. However, it is difficult for the outcomes to generalize because of lack of color reproducibility and image standardization. Our study aims at the exploration of tongue colors classification with a standardized tongue image acquisition process and color correction. Methods. Three traditional Chinese medical experts are chosen to identify the selected tongue pictures taken by the TDA-1 tongue imaging device in TIFF format through ICC profile correction. Then we compare the mean value of L * a * b * of different tongue colors and evaluate the effect of the tongue color classification by machine learning methods. Results. The L * a * b * values of the five tongue colors are statistically different. Random forest method has a better performance than SVM in classification. SMOTE algorithm can increase classification accuracy by solving the imbalance of the varied color samples. Conclusions. At the premise of standardized tongue acquisition and color reproduction, preliminary objectification of tongue color classification in Traditional Chinese Medicine (TCM) is feasible.

The effects of different types of music on electroencephalogram

In order to study the characteristics of electroencephalogram (EEG) induced by different types of music, we selected 58 volunteers (college students) with no musical training experience. Half of them are males and the rest are females. We finally adopted 49 experimental samples for further data processing. The results showed that: (1) music caused the changes of energy intensity in specific area of the brain and related EEG waveforms; (2) different types of music caused the changes of energy intensity in different regions and different EEG waveforms.