Diabetic plantar pressure analysis using image fusion

Plantar pressure images analysis is the key issue of designing comfortable shoe products through last customizing system, which has attracted the researchers’ curiosity toward image fusion as an application of medical and industrial imaging. In the current work, image fusion has been applied using wavelet transform and compared with Laplace Pyramid. Using image fusion rules of Mean-Max, we presented a plantar pressure image fusion method employing haar wavelet transform. It was compared in different composition layers with the Laplace pyramid transform. The experimental studies deployed the haar, db2, sym4, coif2, and bior5.5 wavelet basis functions for image fusion under decomposition layers of 3, 4, and 5. Evaluation metrics were measured in the case of the different layer number of wavelet decomposition to determine the best decomposition level and to evaluate the fused image quality using with different wavelet functions. The best wavelet basis function and decomposition layers were selected through the analysis and the evaluation measurements. This study established that haar wavelet transform with five decomposition levels on plantar pressure image achieved superior performance of 89.2817% mean, 89.4913% standard deviation, 5.4196 average gradient, 14.3364 spatial frequency, 5.9323 information entropy and 0.2206 cross entropy

Stomatal Conductance and Morphology of Arbuscular Mycorrhizal Wheat Plants Response to Elevated CO2 and NaCl Stress

Stomata play a critical role in the regulation of gas exchange between the interior of the leaf and the exterior environment and are affected by environmental and endogenous stimuli. This study aimed to evaluate the effect of the arbuscular mycorrhizal (AM) fungus, Rhizophagus irregularis, on the stomatal behavior of wheat (Triticum aestivum L.) plants under combination with elevated CO2 and NaCl stress. Wheat seedlings were exposed to ambient (400 ppm) or elevated (700 ppm) CO2 concentrations and 0, 1, and 2 g kg−1 dry soil NaCl treatments for 10 weeks. AM symbiosis increased the leaf area and stomatal density (SD) of the abaxial surface. Stomatal size and the aperture of adaxial and abaxial leaf surfaces were higher in the AM than non-AM plants under elevated CO2 and salinity stress. AM plants showed higher stomatal conductance (gs) and maximum rate of gs to water vapor (gsmax) compared with non-AM plants. Moreover, leaf water potential (Ψ) was increased and carbon isotope discrimination (Δ13C) was decreased by AM colonization, and both were significantly associated with stomatal conductance. The results suggest that AM symbiosis alters stomatal morphology by changing SD and the size of the guard cells and stomatal pores, thereby improving the stomatal conductance and water relations of wheat leaves under combined elevated CO2 and salinity stress

Morphological segmentation analysis and texture-based support vector machines classification on mice liver fibrosis microscopic images

Background To reduce the intensity of the work of doctors, pre-classification work needs to be issued. In this paper, a novel and related liver microscopic image classification analysis method is proposed. Objective For quantitative analysis, segmentation is carried out to extract the quantitative information of special organisms in the image for further diagnosis, lesion localization, learning and treating anatomical abnormalities and computer-guided surgery. Methods in the current work, entropy based features of microscopic fibrosis mice’ liver images were analyzed using fuzzy c-cluster, k-means and watershed algorithms based on distance transformations and gradient. A morphological segmentation based on a local threshold was deployed to determine the fibrosis areas of images. Results the segmented target region using the proposed method achieved high effective microscopy fibrosis images segmenting of mice liver in terms of the running time, dice ratio and precision. The image classification experiments were conducted using Gray Level Co-occurrence Matrix (GLCM). The best classification model derived from the established characteristics was GLCM which performed the highest accuracy of classification using a developed Support Vector Machine (SVM). The training model using 11 features was found to be as accurate when only trained by 8 GLCMs. Conclusion The research illustrated the proposed method is a new feasible research approach for microscopy mice liver image segmentation and classification using intelligent image analysis techniques. It is also reported that the average computational time of the proposed approach was only 2.335 seconds, which outperformed other segmentation algorithms with 0.8125 dice ratio and 0.5253 precision

Multi-Source Information Fusion Model In Rule-Based Gaussian-Shaped Fuzzy Control Inference System Incorporating Gaussian Density Function

An increasing number of applications require the integration of data from various disciplines, which leads to problems with the fusion of multi-source information. In this paper, a special information structure formalized in terms of three indices (the central presentation, population or scale, and density function) is proposed. Single and mixed Gaussian models are used for single source information and their fusion results, and a parameter estimation method is also introduced. Furthermore, fuzzy similarity computing is developed for solving the fuzzy implications under a Mamdani model and a Gaussian-shaped density function. Finally, an improved rule-based Gaussian-shaped fuzzy control inference system is proposed in combination with a nonlinear conjugate gradient and a Takagi-Sugeno (T-S) model, which demonstrated the effectiveness of the proposed method as compared to other fuzzy inference systems

Co-creation of Social Innovation: Corporate Universities as Innovative Strategies for Chinese Firms to Engage with Society

Corporate social innovation is a novel, strategic means for enterprise to establish competitive advantage through collaboration with powerful stakeholders, like governments, where firms are simultaneously able to meet social needs and benefit themselves. There is, however limited empirical research investigating how such collaboration enables the co-creation of innovation between firms and society, particularly in emerging markets. In response, this paper takes corporate universities (CUs), a typical manifestation of corporate social innovation, as an example, and explores whether CUs encourage employees to engage in innovation and pro-environmental behaviors, thereby contributing to their firms and local communities in China. Using quantitative methods, we found that employees’ participation in CU training/education courses significantly affects employees’ innovation and environmentally-friendly behaviors in both work and life, and that it enhances their normative commitment (NC) to organizations. Moreover, this commitment mediates employees’ participation in the university program, in terms of both their innovation and pro-environmental behaviors. The main contribution of this paper is to enrich the innovation literature by suggesting a fresh, co-creation mechanism of social innovation between enterprise and government, while offering valuable first-hand evidence in a non-Western context. Our results allow policy makers and stakeholders to gain an in depth understanding of relevant issues

Image Feature-Based Affective Retrieval Employing Improved Parameter And Structure Identification Of Adaptive Neuro-Fuzzy Inference System

Affective computing has various challenges especially for features extraction. Semantic information and vocal messages contain much emotional information, while extracting affective from features of images, and affective computing for image dataset are regarded as a promised research direction. This paper developed an improved adaptive neuro-fuzzy inference system (ANFIS) for images’ features extraction. Affective value of valence, arousal, and dominance were the proposed system outputs, where the color, morphology, and texture were the inputs. The least-square and k-mean clustering methods were employed as learning algorithms of the system. This improved model for structure and parameter identification of ANFIS were trained and validated. The training errors of the system for the affective values were tested and compared. Data sources grouping and the ANFIS generating processes were included. In the network training process, the number of input variables and fuzzy subset membership function types has been relative to network model under different affective inputs. Finally, well-established training model was used for testing using International Affective Picture System. The resulting network predicted those affective values, which compared to the expected outputs. The results demonstrated the effect of larger deviation of the individual data. In addition, the relationships between training errors, fuzzy sample set, training data set, function type, and the number of membership functions were illustrated. The proposed model showed the effectiveness for image affective extraction modeling with maximum training errors of 14 %

Convolutional Neural Network Based Clustering And Manifold Learning Method For Diabetic Plantar Pressure Imaging Dataset

Foot plantar pressure characteristics can be used to investigate and characterize diabetic patients. The current work proposed an effective method for analyzing plantar pressure images in order to obtain the key areas of foot plantar pressure characteristics. A collected data of plantar pressure of diabetic patients is involved to evaluate the proposed method based on image analysis. Initially, the plantar pressure imaging dataset was preprocessed by using watershed transformation to determine the region of interest (ROI) as well as to decrease the computation complexity. Afterward, the convolutional neural network (CNN) based K-mean clustering and parameterized manifold learning using an improved isometric mapping algorithm (ISOMAP) were applied to attain segments of the imaging dataset. The proposed method was discussed and was compared on ten areas of plantar including toes, mid-foots and heels. For the clustering result, the experiments established superior performance with root mean square error (RMSE) of 70%, average accuracy of 80% and 80% time consuming. Furthermore, the proposed manifold learning method achieved an average accuracy of 87.2%, which was superior to other seven algorithms including multi-dimensional scaling (MDS), principal components analysis (PCA), locally linear embedding (LLE), Hessian LLE, Laplacian eigenmap method (LE), diffusion map, and local tangent space alignment (LTSA). The proposed approach established potential application on shoe-last customization of diabetic foot

Image Fusion Incorporating Parameter Estimation Optimized Gaussian Mixture Model And Fuzzy Weighted Evaluation System: A Case Study In Time-Series Plantar Pressure Data Set

The key issue in image fusion is the process of defining evaluation indices for the output image and for multi-scale image data set. This paper attempted to develop a fusion model for plantar pressure distribution images, which is expected to contribute to feature points construction based on shoe-last surface generation and modification. First, the time series plantar pressure distribution image was preprocessed, including back removing and Laplacian of Gaussian (LoG) filter. Then, discrete wavelet transform and a multi-scale pixel conversion fusion operating using a parameter estimation optimized Gaussian mixture model (PEO-GMM) were performed. The output image was used in a fuzzy weighted evaluation system, that included the following evaluation indices: mean, standard deviation, entropy, average gradient, and spatial frequency; the difference with the reference image, including the root mean square error, signal to noise ratio (SNR), and the peak SNR; and the difference with source image including the cross entropy, joint entropy, mutual information, deviation index, correlation coefficient, and the degree of distortion. These parameters were used to evaluate the results of the comprehensive evaluation value for the synthesized image. The image reflected the fusion of plantar pressure distribution using the proposed method compared with other fusion methods, such as up-down, mean-mean, and max-min fusion. The experimental results showed that the proposed LoG filtering with PEO-GMM fusion operator outperformed other methods

The Pleiotropic Regulator AdpA Regulates the Removal of Excessive Sulfane Sulfur in <i>Streptomyces coelicolor</i>

Reactive sulfane sulfur (RSS), including persulfide, polysulfide, and elemental sulfur (S8), has important physiological functions, such as resisting antibiotics in Pseudomonas aeruginosa and Escherichia coli and regulating secondary metabolites production in Streptomyces spp. However, at excessive levels it is toxic. Streptomyces cells may use known enzymes to remove extra sulfane sulfur, and an unknown regulator is involved in the regulation of these enzymes. AdpA is a multi-functional transcriptional regulator universally present in Streptomyces spp. Herein, we report that AdpA was essential for Streptomyces coelicolor survival when facing external RSS stress. AdpA deletion also resulted in intracellular RSS accumulation. Thioredoxins and thioredoxin reductases were responsible for anti-RSS stress via reducing RSS to gaseous hydrogen sulfide (H2S). AdpA directly activated the expression of these enzymes at the presence of excess RSS. Since AdpA and thioredoxin systems are widely present in Streptomyces, this finding unveiled a new mechanism of anti-RSS stress by these bacteria