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In this paper, the three-dimensional segmentation of pulmonary honeycomb lesions and the function of image biomarkers are integrated into a quantitative analysis module of the lung, and integrated into the PACS image diagnosis workstation for the quantitative analysis of doctors. It is difficult to segment honeycomb lesions in lung CT images because of its diffuse and blurred edges, but it has good texture characteristics. Based on this feature, this paper converts honeycomb lesions into a texture classification problem. Firstly, the lung parenchyma is extracted by automatic threshold segmentation and region growing method, and then the lung parenchyma is divided into several small regions according to the texture by watershed method. Then, according to the texture characteristics of each small area, the trained support vector machine is used to classify. Finally, the correlation between gray level and spatial position of slice data is used to correct the classification results, so as to reduce false positive areas. In order to expand the study of imaging biomarkers of chronic lung diseases to more extensive major diseases, a sample database was established for a wide range of multiple lesions. Through feature extraction and feature analysis of multiple lesions in database, potential feature differences can be excavated, which lays a solid foundation for further study of image biomarkers of multiple lesions.

Long-term exposure to greenhouse environments exposes greenhouse workers to inhalation of antigens that can cause respiratory diseases. This study was conducted to investigate the prevalence and potential risk factors for bronchial asthma among the Chinese greenhouse workers based on questionnaire and spirometry data. This was an observational cross-sectional study, performed via stratified-cluster-random sampling. It was conducted in Liaoning Province from the northeast of People’s Republic of China, using a population-based sample of 5,880 workers at 835 plastic film greenhouses. All subjects were interviewed using a standardized questionnaire and underwent pulmonary function tests. Multiple logistic regression analysis was conducted to assess associations between self-reported factors of greenhouse worker exposure and bronchial asthma and to identify potential risk factors for this disease. A total of 5,420 questionnaires were completed. The overall prevalence of asthma in greenhouse workers was 19.2% (1040/5420). Multiple logistic regression analysis revealed that the use of multiple pesticides (odds ratio [OR] 1.24, 95% confidence interval [CI] 1.03–1.49), bad odors in the greenhouse (OR = 1.26, 95% CI = 1.07–1.49), and report of the onset of cough when entering the greenhouse (OR = 1.25, 95% CI = 1.09–1.44) were associated with the development of asthma. In contrast, a higher body mass index (BMI >18.5 kg/m2, OR = 0.93, 95% CI = 0.90–0.95), planting flowers (OR = 0.92, 95% CI = 0.87–0.98), open sidewall to outside (natural ventilation) for at least 30 min per event (OR = 0.82, 95% CI = 0.69–0.96), living in greenhouse (OR = 0.85, 95% CI = 0.73–0.99), and experiencing cough before 14 years old (OR = 0.61, 95% CI = 0.43–0.84) were protective factors to the presentation of asthma among greenhouse workers. Our results suggest that asthma is a major public health problem among Chinese greenhouse workers and more attention should be devoted to preventive measures and management of this disease.

With respect to the limited field of view in the visual environment sensory perceptual system of the traditional three-dimensional reconstruction system, as well as such defects as high price, big size, cumbersome shape and low rate of imaging, a three-dimensional reconstruction system is designed based on panoramic vision and a two-dimensional ranging laser. By adopting the linear laser technology, a non-contact hardware system of laser scanning and measuring system is constructed based on panoramic vision, so as to achieve real-time scanning of objects; by adopting the corner detection technology to abstract image features, and to perform image matching and camera calibration, a three-dimensional model of objects is constructed. System software tests developed in the programming environment of Visual C++ show that this system could realize an excellent three-dimensional reconstruction based on panoramic vision and two-dimensional ranging laser.

Chronic obstructive pulmonary disease (COPD) has seriously impacted the health of individuals and populations. In this study, proton nuclear magnetic resonance ((1)H NMR)-based metabonomics combined with multivariate pattern recognition analysis was applied to investigate the metabolic signatures of patients with COPD. Serum and urine samples were collected from COPD patients (n = 32) and healthy controls (n = 21), respectively. Samples were analyzed by high resolution (1)H NMR (600 MHz), and the obtained spectral profiles were then subjected to multivariate data analysis. Consistent metabolic differences have been found in serum as well as in urine samples from COPD patients and healthy controls. Compared to healthy controls, COPD patients displayed decreased lipoprotein and amino acids, including branched-chain amino acids (BCAAs), and increased glycerolphosphocholine in serum. Moreover, metabolic differences in urine were more significant than in serum. Decreased urinary 1-methylnicotinamide, creatinine and lactate have been discovered in COPD patients in comparison with healthy controls. Conversely, acetate, ketone bodies, carnosine, m-hydroxyphenylacetate, phenylacetyglycine, pyruvate and α-ketoglutarate exhibited enhanced expression levels in COPD patients relative to healthy subjects. Our results illustrate the potential application of NMR-based metabonomics in early diagnosis and understanding the mechanisms of COPD.